esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposes a fully automatic procedure for brain tumor segmentation from multi-spectral magnetic resonance image data. The procedure consists of three main phases. The first phase prepares the data to be suitable for supervised classification, via noise suppression, handling missing data, histogram normalization, feature generation, and an optional atlas-based data enhancement. In the second phase, an initial labeling of the pixels is achieved, using supervised classification performed by an ensemble of binary decision trees. In the final phase, the intermediary pixel labels are refined using a random forest-based reclassification and a structural post-processing step that investigates contiguous regions within the detected tumor. The proposed procedure is trained and evaluated using all the MRI records of the BraTS 2015 and BraTS 2019 training data sets.  
With average Dice similarity coefficients of up to 85.6% and overall Dice scores approaching 88%, as well as the whole processing time of an MRI record being below one minute without needing a GPU for high-speed computations, the proposed procedure is in competition with state-of-the-art methods. The segmentation quality achieved by the procedure could still improve with the use of more sophisticated handcrafted features in the decision ensemble, while the implementation of a feature selection scheme could be beneficial to the efficiency of the proposed procedure.  
Conceptualization, A.G., L.S., and L.K.; methodology, A.G. and L.S.; software, A.G. and L.S.; validation, A.G. and L.S.; formal analysis, L.K.; investigation, A.G. and L.S.; resources, L.K.; data curation, A.G.; writing, original draft preparation, A.G. and L.S.; writing, review and editing, A.G. and L.S.; visualization, L.S.; supervision, L.S.; project administration, L.K.; funding acquisition, L.K. All authors read and agreed to the published version of the manuscript.  
Research supported in part by Sapientia Institute for Research Programs (KPI). The work of [PERSON] was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No 679681). The work of [PERSON] was supported by the Janos Bolyai Fellowship Program of the Hungarian Academy of Sciences and by the UNKP-20-5 New National Excellence Program of the Ministry of Innovation and Technology from the Source of the National Research, Development and Innovation Fund.  
Not applicable.  
The brain MRI records processed in this study are available at the BraTS website: [[http://www.braintumorsegmentation.org](http://www.braintumorsegmentation.org)]([http://www.braintumorsegmentation.org](http://www.braintumorsegmentation.org)).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ACC accuracy  
BDT binary decision tree  
BraTS Brain Tumor Segmentation (Challenge)  
CNN convolutional neural network  
CPU central processing unit  
CT computed tomography  
DICOM digital imaging and communications in medicine  
DSC Dice similarity score  
ERT extremely randomized trees  
FCM fuzzy \(c\)-means
## 3 The Modeling and Simulation Architecture  
### Work Environments  
The simulation environment is divided into two parts. The first part contains all the elements used to simulate a WSAN, and the second part is made up of the components to be deployed in the cloud platform. Some of the concerns related to distributed application modeling can be addressed by using elements that follow the principles of reactive manifesto. These principles help to design reactive applications that use observable models, event streams, and stateful clients [8]. Figure 1 shows the architectural components and their interrelationships. This architecture has real-time features that are essential to meet some needs of IoT applications. The set of components deployed in the cloud platform can be used as a common architecture for data transfer of multiple applications and services in a smart city.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Simulator** & **Ref.** & **Category** & **Language** & \begin{tabular}{c} **Modelling** \\ **IoT Nodes** \\ \end{tabular} & \begin{tabular}{c} **Cloud** \\ **Layer** \\ \end{tabular} & \begin{tabular}{c} **Application** \\ **Layer** \\ \end{tabular} \\ \hline DPWSim & [10] & Full Stack Simulator & Java & N & N & Y \\ iFogSim & [11] & Full Stack Simulator & Java & N & Y & Y \\ IOTSim & [12] & Big Data Processing & Java & N & Y & Y \\ SimIoT & [13] & Big Data Processing & Java & N & Y & Y \\ OMNeT++ & [14] & Network Simulator & C++, Java, C\# & Y & N & N \\ ns-3 & [15] & Network Simulator & C++, Python & Y & N & N \\ NetSim & [16] & Network Simulator & Java & Y & N & N \\ QualNet & [17] & Network Simulator & C/C++ & Y & N & N \\ Our hybrid & \
\begin{tabular}{c} [This] \\ solution \\ \end{tabular} & An approach to Full Stack simulator &
\begin{tabular}{c} Java, JavaScript, \\ Scala \& HTML5 \\ \end{tabular} & Y & Y & Y \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of tools to support Internet of Things (IoT) simulation. Y: Supported, N: Not Supported.  
Through our approach, it is feasible to run several simulation files of the model of an IoT system. The simulation can scale through simulation files with a hierarchical structure of several IoT models on the same computer. Additionally, it is possible to scale the simulation further with other computers where the simulation XML files can be run.
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
## 5 Conclusions  
Based on IM, this paper proposed a new random subcarrier-selection method, RSCS-IM, which achieved secure and precise wireless transmission. Compared to PSS Plus RP, which was proposed in [18], RSCS-IM not only enhanced the SE but also reduced complexity. At the same SE, the proposed RSCS-IM scheme featured better BER performance. Moreover, the BER performance of an eavesdropper could be limited by selecting a proper value of  
Figure 11: BER performance versus \(E_{\rm b}/N_{0}\) of [PERSON].  
Figure 12: BER performance versus SNR of [PERSON] under RSCS-IM and PSS Plus RP schemes with different \(N_{T}\).  
\(N_{s}/N_{T}\). Finally, we derived the closed-form expression of BER for the desired position and demonstrated the performance of our scheme via numerical simulation.  
Conceptualization, X.L.; methodology, X.L., J.C. and T.Z.; software, T.Z. and J.C.; validation, T.Z.; formal analysis, T.Z.; investigation, T.Z.; resources, T.Z.; data curation, T.Z.; writing--original draft preparation, T.Z.; writing--review and editing, X.L. and J.C.; visualization, T.Z.; supervision, X.L. and S.L.; project administration, X.L. and S.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key R&D Program of China (2020 YFB1806607) and the Foundation of stability support plan for National Key Lab (IFN2020104).  
Not applicable.  
The authors declare no conflict of interest.
# Parasitic Motions of 3-PRS Parallel Mechanisms with Two Different Branch Chain Arrangements
## 3 3-PRS PM II
### Parasitic Motion Analysis  
The model shown in Figure 12 was established in SolidWorks, and the structural parameters are given in Table 4.  
The ranges of the kinematic parameters \(\psi\) and \(\theta\) are also \([-60^{\circ},\,60^{\circ}]\), which fully satisfies the rotation angles required by the PM.  
When the structural parameters \(e\) of side \(O_{\text{m}}S_{3}\) of the moving platform \(S_{1}S_{2}S_{3}\) are given as 250 mm, 175 mm, and 100 mm and the ranges of the kinematic parameters \(\psi\) and \(\theta\) are given as \([-60^{\circ},\,60^{\circ}]\), the parasitic motion \(x\) can be obtained through MATLAB, as shown in Figure 16.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Length of side \(O_{\text{B}}S_{2}\) of the fixed platform \(B_{1}B_{2}B_{3}\), \(a\) [mm] & 125 \\ Length of side \(O_{\text{B}}B_{3}\) of the fixed platform \(B_{1}B_{2}B_{3}\), \(b\) [mm] & 150 \\ Length of side \(O_{\text{m}}S_{2}\) of the moving platform \(S_{1}S_{2}S_{3}\), \(c\) [mm] & 80 \\ Length of side \(O_{\text{m}}S_{3}\) of the moving platform \(S_{1}S_{2}S_{3}\), \(e\) [mm] & 60 \\ Range of the equivalent link length of the pairs \(P_{1}\), \(d_{1}\) [mm] & [105, 195] \\ Ranges of the kinematic parameters \(\psi\) and \(\theta\) & \([-60^{\circ},\,60^{\circ}]\) \\ \hline \end{tabular}
\end{table}
Table 4: Structural parameters of 3-PRS PM II.  
Figure 15: Diagram of branch chain distribution.  
The CPU running times of parasitic motions \(x\) with the length of side \(O_{m}S_{3}\) of the moving platform \(S_{1}S_{2}S_{3}\) given as 250 mm, 175 mm, and 100 mm and obtained through MATLAB are 2.1060 s, 1.8564 s, and 1.6848 s.  
When given the ranges of the kinematic parameters \(\psi\) and \(\theta\), the maximum values of the parasitic motions \(x\) with the circumradius \(e\) = 250 mm and \(e\) = 100 mm of the moving platform \(S_{1}S_{2}S_{3}\) are obtained as outlined in Table 5.  
When the ranges of the structural parameters \(e\) of the moving platform \(S_{1}S_{2}S_{3}\) are given as [35, 350] mm, the values of the parasitic motions \(x\) with the given \(\psi\) and \(\theta\) parameters have a linear relationship with the circumradius \(e\), as shown in Figure 17.
# Hierarchical Fuzzy MCDA Multi-Risk Model for Detecting Critical Urban Areas in Climate Scenarios
###### Abstract
The fuzzy AHP (F-AHP) method [18] is used in a MCDA model applied in [22] to assess the heatwave vulnerability in Indian districts using demographic and socioeconomic data. A MCDA model based on the fuzzy AHP-WIKOR method [23] is applied in [24] to the districts of Alexandria, in Egypt, to detect those urban districts most at risk of flooding; various geomorphological, infrastructural, environmental, and socio-economic indicators are used in the model. The main limitation of these models is their complexity; the number of indicators is too high; therefore, getting all the data to measure them can be difficult. A GIS-based framework for climatic multi-risk assessment in urban areas is applied in the Metropolitan City of Venice (Italy) in [25] to detect areas and receptors at risk, considering storm surge, pluvial flooding, heat waves, and drought hazard scenarios. The main limitation of this model is its portability, due to the difficulty of finding high-resolution data; this can lead to inaccurate assessments of the most vulnerable urban areas. An AHP-based multicriteria assessment of climatic multi-risks by considering urban flooding, coastal flooding, and heat wave hazard scenarios is applied in [26] to the coastal municipality of Jesolo (Italy). Satellite indices connected to vegetation health, soil impermeability, altitude, and population concentration are used to evaluate the joint vulnerability of the urban settlement to the three phenomena. The main strength of this approach is its high portability for different urban settlements; on the other hand, it uses information on a too small scale, which can affect the accuracy of the assessments. In this research, we propose a GIS-based MCDA model of the urban areas most vulnerable to the two types of climate hazards, heatwaves and pluvial flooding, treated as independent events, partitioning the physical urban system into two subsystems: buildings and open spaces. To guarantee a trade-off between the accuracy of the results and the portability of the model, it adopts the fuzzy-based hierarchical MCDA method [6] and takes into consideration the characteristics that affect the vulnerability of buildings and ranges open to the two types of hazards proposed in [5]. In the following section, the GIS-based multicriteria model presented in [6] and the vulnerability assessment model of urban settlements to heatwave and rain flooding phenomena proposed in [5] are first briefly described. Then, the proposed assessment model of the areas of the area of study most vulnerable to both phenomena is described. Section 3 reports the results of the testing of our method on an area of study consisting of the eastern area of the city of Naples (Italy). The concluding discussions are reported in Section 4.
## Abbreviations  
\begin{tabular}{l l} URLLC & Ultrareliable and Low-Latency Communication \\ QoS & Quality of Service \\ QoE & Quality of Experience \\ QoT & Quality of Task \\ E2E & End to end \\ SDN & Software-Defined Network \\ MEC & Mobile Edge Computing \\ ECI & Edge Computational Intelligence \\ \end{tabular}
## 3 Verification of the Proposed Statistical Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Transient Simulation 1**} & \multicolumn{2}{c}{**The Proposed Method 2**} \\  & \multicolumn{2}{c}{**(@BER 10\({}^{-4}\))**} & \multicolumn{2}{c}{**The Proposed Method 2**} \\ \cline{2-5}  & **Eye-Height** & **Sim. Time** & **Eye-Height** & **Est. Time** \\ \hline
1 Buffers, 1 Gb/s & 1.108 V & 15.81 s & 1.138 V & 3.52 s \\
4 Buffers, 1 Gb/s & 1.063 V & 78.32 s & 1.034 V & 6.75 s \\
4 Buffers, 2 Gb/s & 1.045 V & 80.11 s & 1.011 V & 8.10 s \\
4 Buffers, 8 Gb/s & 0.619 V & 3487 s & 0.599 V & 26.8 s \\
16 Buffers, 2 Gb/s & \multicolumn{2}{c}{Fail to sim.} & 0.701 V & 95.11 s \\ \hline \hline \end{tabular}  
* Workstation specification: Intel(R) Core(TM) i7-8700 CPU @ 3.20 GHz, 64.0 GB Installed memory (RAM). 2 Est. time: HSPICE step-responses simulation plus MATLAB calculation time.  
\end{table} 
Table 2: Accuracy and simulation time (estimation time for the proposed method) comparisons between HSPICE transient simulation and the proposed method. Figure 9: Total of four buffers operating at a data rate of 2 Gb/s. (**a**) HSPICE simulation result. (**b**) Estimated statistical eye-diagram using the proposed method. Previous works [20; 21] proposed the statistical eye-diagram estimation method considering power/ground noise generated by SSO for the first time. However, it does not consider steady states affected by SSO. In Figure 11, statistical eye-diagrams estimated based on previous methods [20; 21] and the proposed method are compared. Without consideration of the steady states affected by SSO, estimated statistical eye-diagrams have inaccurate contours, rising/falling edges, and steady states. In the following section, the proposed method is used to SI/PI design in the high-speed interconnect. Figure 11: Comparison between statistical eye-diagrams estimated based on previous methods [20; 21] (**a**) and estimated based on the proposed method. (**b**) Total of 16 buffers are in operation, generating SSO effects at the data rate of 1 Gb/s. Figure 10: Estimated eye-diagrams based on the PDA method are plotted. (**a**) four buffers operating at 1 Gb/s and (**b**) four buffers operating at 2 Gb/s. Compared to Figures 8 and 9, the PDA method is not capable of estimating an accurate eye-diagram under the influence of power/ground noise.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### UC1: Rural Environment
#### 5.1.2 Rural Serving Distance  
Figure 9 shows the distribution of the serving distances for the CPE-EN and between EN-EN. The serving distance of each link helps to determine how the system is optimised. The \(p_{90}\) serving distance between CPE and ENs for FA scenario is 130 m while 140 m for LP, which is reduced for FL down to 70 m. This is due to the optimisation function to maximise user coverage and reduce path loss, reducing service distance.  
In the case of the distance between EN-EN, the distribution behaves differently due to the minimisation function of the network infrastructure. Here, 90% of ENs are connected within 100 m. In addition, the heavy rain reduced the connection distance by 18% and 8% in light rain. Similarly, vegetation reduces the \(p_{90}\) distance by 10% in the backhaul network and only 5% in the access network.  
Figure 8: User coverage and network utilisation in UC 1.
## 3 Results
### Real-World Applications and the Impact of JAVIS in Hospital Workflows
#### 3.8.3 AI-Assisted Research and Software Development in Restricted Environments  
A significant challenge in hospital-based research environments is the lack of Internet access within internal networks, which prevents researchers from utilizing external AI-powered tools such as StackOverflow, ChatGPT, or cloud-based LLMs. Before the deployment of JAVIS, researchers had to manually retrieve AI-generated code snippets from external computers and transfer them via text files, significantly slowing down the development process.  
With the integration of JAVIS, researchers now have direct access to AI-powered coding assistance within the hospital network, enabling them to generate, refine, and debug code in real-time without reliance on external services. This has accelerated research productivity and streamlined software development workflows. Furthermore, JAVIS has been utilized in its own iterative improvement process, where LLM-generated code snippets have been used to facilitate non-critical system updates, demonstrating its practical utility as an AI-assisted development environment within secure hospital networks.  
Additionally, the ability to host multiple models concurrently has enabled research teams to conduct comparative studies on AI models using sensitive hospital data, an approach that was previously impossible due to cloud-based restrictions. By providing a scalable, multi-model deployment infrastructure, JAVIS has facilitated research initiatives requiring strict data privacy compliance, allowing hospitals to conduct LLM benchmarking and validation studies without compromising patient confidentiality.
# VPP Participation in the FCR Cooperation Considering Opportunity Costs
## 2 Methodology
### Scenarios  
The scenarios examined in this study were formulated by employing distinct values for user-defined variables outlined in Table 1, and they are detailed in Table 2.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Scenario 1**} & \multicolumn{2}{c}{**Scenario 2**} & \multicolumn{2}{c}{**Scenario 3**} \\  & \multicolumn{2}{c}{**”Pessimistic”**} & \multicolumn{2}{c}{**“Expected”**} & \multicolumn{2}{c}{**“Pledged”**} \\  & \multicolumn{2}{c}{**2025**} & \multicolumn{2}{c}{**2025**} & \multicolumn{2}{c}{**2030**} \\ \hline
**Variable** & **PT** & **ES** & **PT** & **ES** & **PT** & **ES** \\ \hline \(VGS_{c}\) & 3\% & 2\% & 6\% & 5\% & 12\% & 10\% \\ \(RGS_{c}\) & 20\% & 20\% & 20\% & 20\% & 20\% & 20\% \\ \(PN_{c}\) [MW] & 50 & 200 & 100 & 500 & 1000 & 2000 \\ \(HMin\) [MW] & 200 & 1000 & 200 & 1000 & 500 & 2000 \\ \(HMax\) [MW] & 2000 & 10,000 & 2000 & 10,000 & 5000 & 20,000 \\ \(RDS_{c}\) & 20\% & 20\% & 20\% & 20\% & 20\% & 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenarios used in simulations.  
Figure 8: Monthly average values of the CBMP.  
The main difference between the scenarios was the installed capacity of HEs. Portugal has committed to installing 2 to 2.5 GW of HEs by 2030 [23], whereas Spain has committed to 4 GW [24]. This study investigated this scenario (Scenario 3 \"pledged\" 2030), as well as two scenarios (1 and 2), assuming that by mid-decade, only a small part (5-10%) of the 2030 targets were achieved. \(PN_{c}\) refers only to HEs producing _electricity-based hydrogen_: it was assumed that by 2030, 50% of the installed HEs were grid-connected. For scenario 3, the time series of wind and solar energy generation, i.e., \(G_{c,t}\), was increased proportionally to the expected increase in solar and wind power in both countries in 2030 [25; 26]. The assumption was made that both \(RGS_{c}\) and \(RDS_{c}\) were set at 20%.
# Ground Deformation Associated with Deep Excavations in Beijing, China
## 3 Characteristic Analysis of Deformation
### Factors Influencing Maximum Surface Deformation
#### 3.3.2 Length-Width Ratio  
[PERSON] et al. [8] found in their study that the length-width ratio of foundation pits has a significant influence on excavation stability. Several studies conducted in the same region have shown that the least deformation is observed in circular foundation pits, followed by elliptical pits, and finally, square pits.  
A strong correlation between \(\delta vm\) and the length-width ratio (Pearson's r = 0.8, \(p\) < 0.05) was found under stratum conditions in Beijing (Figure 9). The \(\delta vm\) positively correlated with \(H\), and the trend is obvious. Actual experiences also showed that the deformation was well controlled when the level of earth excavation at each step, and the time without support, were reduced ([PERSON] et al. [25]).
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 7 Open Challenges
### East West Bound Management  
With increase in sensor nodes to hundreds or thousands, use of a single controller becomes inefficient in terms of maintaining a global network view and data collection therefore proposals have been made towards using multiple controllers to solve this issue [28]. SDN-based management of communication protocols between distributed controllers in the control plane while maintaining a logically centralized and global view of the entire wireless sensor network remains an open challenge. This includes development of East-West bound APIs for efficient control of data flow in the data plane.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Management Scheme** & **Energy Efficiency** & **Robustness** & **Scalability** & **Adaptability** \\ \hline Sensor OpenFlow [20, 21] & - & Yes & Yes & Yes \\ SDWN [60] & Yes & Yes & - & Yes \\ Smart [14] & Yes & No & No & Yes \\ SDN-WISE [78] & Yes & Yes & Yes & Yes \\ SDCSN [88] & Yes & Yes & Yes & Yes \\ TinySDN [69, 118] & Yes & Yes & Yes & Yes \\ Virtual Overlay [59, 87, 90] & - & Yes & Yes & Yes \\ Multi-task [122] & Yes & - & - & Yes \\ SDWSN-RL [123] & Yes & Yes & Yes & Yes \\ Wireless power transfer [126] & Yes & Yes & Yes & - \\ Function alternation [65] & Yes & Yes & Yes & Yes \\ Statistical machine learning [24] & - & Yes & - & - \\ Context-based [91, 92] & - & Yes & Yes & Yes \\ Soft-WSN [9] & Yes & Yes & - & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 5: SDWSN management scheme evaluation against design criteria.
## 2 Materials and Methods
### Remote Sensing Data and Analysis
#### 2.5.4 Regression Analysis for NDVI and LAI/CC  
Empirical regression analysis was performed on the LAI field measurements for both rice groups and the average LAI was used to explore the general pattern of seasonal LAI in the training rice plots. The NDVI to CC conversion was accomplished using an empirical relationship between the NDVI from the HJ-1A/B imagery and the CC (in situ LAI converted to CC using Equation (3)). The regression model was evaluated using trend analysis and the coefficient of determination (R\({}^{2}\)).