esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Cytotoxicity and Viability Assay  
The experiment was conducted on three human cancer cell lines and one normal. All cancer cell lines belonged to the gastrointestinal panel: DLD-1 colon adenocarcinoma (ATCC CCL-221), HCT116 colon carcinoma (ATCC CCL-247), and HT29 colorectal adenocarcinoma (ATCC HTB-38). The normal cell line was the CCD 841 CoN normal human colon epithelial line (CRL-1790). Cells were grown at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) atmosphere, with relative humidity, using as culture medium McCoy's (HT29), DMEM high glucose (HCT116 and DLD-1), or MEM (CCD 841 CoN), with the addition of 10% fetal bovine serum and 1% antibiotics solution. All cell lines, cultures media, and supplements were obtained from Merck (Darmstadt, Germany). The cells were seeded in 96-well plates (1.5 \(\times\) 104 cells/well) for 24 h. Then, the culture medium was replaced with the same medium containing different concentrations of the tested substances from 0 to 200 \(\upmu\)g/mL and incubated for 24 h. The concentration range used in our study is the standard approach, consisting of at least five concentrations. We used the range from 0 \(\upmu\)g/mL (control, untreated cells) to 200 \(\upmu\)g/mL. We did not use higher concentrations as this would indicate only weak activity, if any. Cell viability was measured with an MTT assay, as described previously [38]. DSMO diluted accordingly was also tested, to exclude the false cytotoxic effect. Doxorubicin was used as a reference standard. The absorbance (570 nm) was measured on a microplate reader (BioTek Instruments Inc., Winooski, VT, USA). Cell viability was calculated as % of untreated, control cells, and IC\({}_{50}\) values if possible. Each experiment was performed in triplicate.
# Possible Blockchain Solutions According to a Smart City Digitalization Strategy
## 3 Digitalization Strategy of City Governance
### Data Consolidation  
Consolidation of data is necessary so that all data, whenever possible, are entered only once, through one interface, and written in its original or encrypted form in one place, while other systems will have access to them as needed. It is also necessary to resolve possible unnecessary redundancy and data collisions when consolidating. When this step is properly conducted, there is a possibility to store public data on a blockchain network to ensure data security, transparency, and confidence [39].  
Blockchain technology is robust because a single entity does not control it, the network is decentralized, and there is no single point of failure, as shown in Figure 4. Information is distributed across the nodes in the entire network, and failure in one node will not lead to data loss. Data are secured and trusted, while it is almost impossible to change or false verify data. Intruders should have enormous computational power to alter the stored information. If anyone changes the information, it is visible to all participants, transparency  
Figure 3: Smart City components included in digitalization strategy.  
is achieved, and possible tampering is eliminated. All participants in the transaction should have a consensus before the transaction is validated and data stored in the blockchain.  
After the data consolidation process, we divided data into the following separate categories:  
* Data on the city system (organizational units, employees, hierarchy).
* Data on users of public services (citizens, associations, companies, crafts, etc.).
* Data on city cases (submitted requests, etc.).
* Sensor data--data obtained by various sensors (mostly IoT), and can be referred to as user data related to the basic public services they receive (e.g., water consumption, waste collection, etc.), to City data related to additional public services, i.e., improvement of existing ones (e.g., smart parking, video surveillance for security purposes, but also additional purposes, etc.), and data related to the quality of life (e.g., air quality, etc.).  
We should have in mind and incorporate already existing data:  
* Data available in national databases and registers, as will be explained in the next chapters, which can be accessed by appropriate connectors, e.g., through web services or API (Application Programming Interface).
* Data for specific cases depend on business logic, which can be implemented to a greater or lesser extent in software support for individual processes.
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 6 Conclusions  
In this paper, we propose a multi-agent system (MAS) for addressing train rescheduling problems in large-scale railway networks. We present a mixed-integer linear programming (MILP) formulation for the large-scale train dispatching problem and describe our approach to network decomposition, the local search algorithm, and the collaborative strategy. By decomposing the railway network, the train rescheduling problem is broken down into smaller sub-problems, reducing computational complexity and enabling real-time traffic control. Simulation results demonstrate that the MAS outperforms the First-Come-First-Served (FCFS) routing approach in 19.264% of the tested scenarios, with improvements of up to 34.11% in key control objectives. In all remaining cases, the MAS performs at  
Figure 4: Statistic value of total delay in scenarios in class III.  
Figure 5: Computational time of each agent in the MAS system.  
least as well as FCFS, maintaining solution quality without degradation. The decentralized nature of the MAS significantly reduces computational time, with simulations completed in approximately 0.5 min, making it highly suitable for real-time applications.  
The proposed MAS offers a decentralized alternative to traditional centralized rescheduling methods, improving scalability and adaptability for larger networks. Future work will explore additional operational factors, including train cancellation, replatforming, and the influence of human factors such as crew scheduling and passenger flow dynamics. We will also examine the constraints imposed by existing infrastructure on railway operations and explore potential upgrades to enhance operational flexibility.  
Conceptualization, J.L., C.R. and L.C.; methodology, J.L.; software, J.L.; validation, J.L.; formal analysis, J.L.; investigation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, Z.T. and N.Z.; visualization, J.L.; supervision, L.C.; project administration, C.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The first author acknowledges the support of the Michael Beverley Innovation Fellowship at the University of Leeds.  
The authors declare no conflicts 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.
## 3 Models and Design Goals
### Multicast Subsystem
#### 3.2.2 Multicast Tree  
In our scheme, we take the Degree-dependent Branch-node Weighted Steiner Tree (DBWST) proposed in [21] to construct our multicast tree. Algorithm 1 describes the specific procedure. Based on the DBWST, consider an undirected graph \(G_{v}=\{V,L\}\), in which \(V\) denotes OLDs and other entities taking part in communication in software-defined MIoT, and \(L\) represents a collection of links. For example, the link \(l=(v,w)\in L\) denotes the link from \(v\in V\) and \(w\in V\). Then the cost of link \(l\) is \(Cst(l):L\mapsto R^{+}\), where \(R^{+}\) is nonnegative. Let \(s\) be the source of a multicast and \(U\subset V-\{s\}\) be the set of destination nodes, which in our system is the affected OLDs. The size of this group corresponds to the quantity of \(|R|\). Let \(T=(s,U)\) denote the multicast tree whose source is \(s\) and spanning all nodes \(OLD_{i}\in U\), \(1<i<|U|\). According to the definition of branch node in Steiner Tree, if the degree of node \(OLD_{i}\) is no less than three, then \(OLD_{i}\) would be one. Let \(\pi_{u}\) represent that \(u\) is a branch node in \(T\). Based on the above description, finally the cost of the tree \(T\) can be denoted as:  
\[Cst(T)=\sum_{l\in T}Cst(e)+\sum_{u\in T}\pi_{u}\cdot Cst(u) \tag{1}\]  
Based on the DBWST, it is computationally uncomplicated to find a tree \(T=(s,U)\) which makes the \(Cst(T)\) lowest. The constructed multicast tree can not only help to distribute messages more efficiently, but can also be applied to other fields, such as video conferences and streaming media subscribing.  
```
0: input departure, \(DP\); destination,\(Ds\)
0: reach \(Ds=1\)
1:\(OLD_{i}\) request path
2: control plane return path \(C=\{LC_{k}\}^{*},0<k<n\)
3:\(LC_{k}:ST=ST+\{OLD_{i}\}\)
4:while\(C!=\emptyset\)do
5:\(OLD_{i}\) leaves \(LC_{k}\)
6://\(OLD_{i}\) does:
7:\(C=C-\{LC_{k}\}\), \(1<k\leq n\)
8://\(LC_{k}\) does
9:\(ST=ST-OLD_{i}\), \(0\leq|ST|\leq n\)
10:if\(LC_{k}\) unreachable = true then
11:\(T=DBWST(d,n)\)
12://\(d\) represents the total number of affected OLDs
13://\(0<d\leq m\)
14: jump tp line 1
15:endwhile
```  
**Algorithm 1** Directing Process
## 3 Grassmann MTS Clustering
### Summary of MTS Clustering on Grassmann Manifold  
Figure 4 provides an overview of ARMA-modeled MTS clustering on the Grassmann manifold. The following is a summary of the three approaches discussed in this section.  
* Intrinsic approach: We adopt inherent (dis)similarity for the Grassmann manifold, such as principle angle and Procrustes distance, for spectral clustering algorithms.
* Extrinsic approach: We embed the Grassmann manifolds into an RKHS and canonical correlation Grassmann kernel where clustering distortion is minimized. We flatten the Grassmann manifold via local diffeomorphisms (tangent spaces), i.e., the manifold is locally embedded into Euclidean space, and hence, we perform clustering via spectral clustering techniques in tangent space.
* Semi-intrinsic approach: We perform \(k\)-means clustering on Grassmann manifolds through the use of the Karcher mean. The Karcher mean (minimizer of squared distances between points and the mean on the manifold) is an iterative approach to calculating the mean of a set of points on the manifold. This minimization problem is solved by iteratively moving from manifold to tangent spaces and vice versa.
# A CFD Study of the Hydrodynamic Characteristics of an Autonomous Underwater Helicopter
## 2 Methodology and Numerical Approach
### Methodology
The RNG \(k\)-\(\varepsilon\) turbulence model, which includes the transport equations for turbulent kinetic energy (\(k\)) and its dissipation rate (\(\varepsilon\)), can easily capture the symmetric flow field around the AUH hull. Therefore, RNG \(k\)-\(\varepsilon\) was adopted in this simulation. \[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_{ i})=\frac{\partial}{\partial x_{j}}\Bigg{(}\alpha_{k}\mu_{eff}\frac{\partial k }{\partial x_{j}}\Bigg{)}+G_{k}+G_{b}-\rho\varepsilon-Y_{M}+S_{k} \tag{5}\]  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{i}} (\rho\varepsilon u_{i})=\frac{\partial}{\partial x_{j}}\Bigg{(}\alpha_{ \varepsilon}\mu_{eff}\frac{\partial\varepsilon}{\partial x_{j}}\Bigg{)}+C_{1 \varepsilon}\frac{\varepsilon}{k}(G_{k}+C_{3\varepsilon}G_{b})-C_{2\varepsilon }\rho\frac{\varepsilon^{2}}{k}-R_{\varepsilon}+S_{\varepsilon} \tag{6}\]  
\(G_{k}\) denotes the generation of turbulent kinetic energy attributed to mean velocity gradients, \(G_{b}\) signifies the generation of turbulent kinetic energy resulting from buoyancy, and \(Y_{M}\) indicates the contribution of fluctuating dilatation in compressible turbulence to the total dissipation rate. \(\alpha_{k}\) and \(\alpha_{\varepsilon}\) represent the inverse effective Prandtl numbers for \(k\) and \(\varepsilon\), whereas \(S_{k}\) and \(S_{k}\) denote user-defined source terms. The correlation constants are denoted as \(C_{1\varepsilon}\), \(C_{2\varepsilon}\), and \(C_{3\varepsilon}\). The parameters \(G_{k}\) and \(G_{b}\) are specified as follows:  
\[G_{k}=\mu_{t}\cdot S^{2} \tag{7}\]  
\[G_{b}=\beta g_{i}\frac{\mu_{t}}{\Pr_{t}}\frac  
The total drag force, including friction force, is caused by the surface shear stress of the AUH and the pressure resistance. Therefore, the total drag coefficient (\(C_{d}\)) and the lift coefficient (\(C_{L}\)) are written as follows:  
\[C_{d}=\frac{F_{df}}{\frac{1}{2}\rho U^{2}A_{f}}+\frac{F_{dp}}{\frac{1}{2}\rho U^ {2}A_{f}} \tag{10}\]  
Figure 1:0  
where \(F_{df}\) is the viscous fluid force caused by the surface of the glider, \(F_{dp}\) is the pressure fluid force, \(A_{f}\) is the wetted surface of the AUH, and \(F_{L}\) is the hydrodynamic lift force. ANSYS-FLUENT is useful as the CFD simulation tool. Figures 2 and 3 show a computational zone for the simulation with a length, width, and height of 12 m \(\times\) 4 m \(\times\) 5 m, which is adopted to achieve the simulation results [33]. The coordinate origin was set as the center of the AUH. The inlet velocity is applied to the flow entering the computation domain. The pressure outlet is used on the side opposite the inlet with a pressure outlet. The hull of the AUH is a non-slip wall, and other components use the free slip wall condition. Figure 1:1 The domain of numerical computation. Figure 1:2 The boundary conditions for translation: superior perspective in the Oxz plane and lateral perspective in the Oxy plane.
## 2 Related Work
(2023) [14] address the problem of computation and communication uncertainties in multi-UAV-assisted mobile edge computing (MEC) networks. This paper proposes a robust design to minimize the total weighted energy consumption by jointly optimizing the UAV trajectory, task partition, and resource allocation using a multi-agent proximal policy optimization (MAPPO) with a Beta distribution framework. The numerical results reveal the effectiveness and robustness of the proposed algorithm in minimizing the energy consumption under various uncertainties [15]. These studies reveal that DRL algorithms can adapt to changing environmental conditions and complex problems, thus making them well-suited for disaster scenarios. Optimizing energy consumption in UAV networks is critical for prolonging the operation time and enhancing the overall system efficiency. [PERSON] et al. (2023) [15] address the challenge of maximizing the energy efficiency in a wireless power transfer (WPT)-enabled UAV-assisted emergency communication system. The UAV functions as a base station, performing both communication and wireless charging tasks. The authors propose a low-complexity alternating iterative optimization algorithm that jointly optimizes the UAV trajectory, transmit power, WPT power, and user bandwidth. Their simulations demonstrate that this approach effectively balances the system throughput and UAV energy consumption, significantly improving the energy efficiency compared to the benchmark schemes [16]. [PERSON] et al. (2023) [16] propose an innovative approach for energy-efficient multi-UAV cooperative trajectory optimization. Their multi-agent deep reinforcement learning (MADRL)-based algorithm, called double-stream attention multi-agent actor-critic (DSAAC), significantly improves the communication efficiency and energy savings by leveraging a hierarchical multihead attention encoder and a double data stream network structure in the actor network. The simulation results reveal a notable reduction in energy consumption and an increase in system robustness [17]. The existing body of research emphasizes the potential of UAVs in emergency communication networks and highlights the challenges associated with their deployment. Although the traditional optimization methods have made significant contributions, the advent of reinforcement learning, particularly DRL, offers promising new avenues for research. Our work builds on these foundations, using DRL to develop a robust and adaptive solution to optimize the trajectory of UAVs in disaster scenarios. By addressing the limitations of previous approaches, we aim to provide a comprehensive framework that enhances both coverage and energy efficiency, ultimately improving the resilience and effectiveness of emergency communication networks.
## 1 Introduction  
Anthropometry plays a crucial role in auxology, body composition, and general health. Traditionally, anthropometric assessment of circumferences is a non-invasive and economical evaluation performed by trained professionals with a flexible non-elastic tape measure. However, this method is not free from intra- and inter-rater variability [1, 2], with accuracy and consistency strongly depending on the equipment used, how often it is appropriately standardised/calibrated, the number of personnel performing measurements,frequency of their training, and the overall quality of the local standard operating procedure that also takes into account the practicalities, such as the time of day for measurement [3]. Moreover, whole-body manual assessments can be both time-consuming and have limited applicability when performed on subjects with obesity or excess skin [4].  
Technological advances positively influenced the development of three-dimensional (3D) surface anthropometry by means of photography, photogrammetry, videography, or surface laser scanning. These have been already used for a range of purposes including the manufacture and fitting of customised artificial limbs [5]. Among emerging digital anthropometric technologies, laser scanning and photogrammetry are the most promising. Three-dimensional laser scanning projects laser beams onto the body surface to obtain a volumetric reconstruction, traditionally outperform other digital technologies [6], and has been studied in various settings, such as the assessment of lower limb oedema or monitoring swelling after orthopaedic surgery [7,8]. The generation of 3D digital avatars from photogrammetry could represent a distinct, but complementary, technology, capable of reconstructing a person's body shape using multiple images from different angles [9]. Unlike laser scanning, photogrammetry relies on algorithms that generate a 3D representation of the person, called an avatar, making it potentially more convenient, accessible, and versatile in the health avatar digital field, enabling users to identify changes in body shapes [10]. However, its validity in terms of anthropometric circumferences compared to traditional manual assessment remains to be established.  
Reliability and agreement are data integrity metrics important in method development and quality assurance. Reliability measures include the intraclass correlation coefficient (ICC), which can be used to evaluate inter-method consistency when multiple methods are involved in assessing the same subject, while agreement measures include proportions of specific agreement, random error, coefficient of variation (CV), and Bland-Altman analysis. Previously, these metrics have already been used to evaluate the validity of different digital anthropometric technologies [8]. The aim of this study is to evaluate these integrity metrics derived from the comparison between measurements collected manually with a tape measure and those automatically derived from a 3D digital avatar generated by photogrammetry. This article adhered to the guidelines for reporting reliability and agreement studies (GRRAS) [11].
# Mitigating Urban Congestion: A Cooperative Reservation Framework for Automated Vehicles
## 7 Experimentation and Results
### Increasing the Number of Agents  
The experimental evaluation revealed a constraint on the maximum number of concurrent simulated agents. Given the actor-based architecture, where each vehicle container, governed by the framework detailed in [31], is simulated independently, the current hardware configuration limits the system to four to six concurrent vehicles. For the sake of clarity, at most four CCAVs are used in the experimentation phase to present the signal graphs. Ideally, each vehicle container would be deployed on a dedicated machine networked with the simulation server to ensure automated control, particularly as cooperative modules are integrated within each actor and need to be tested as well. However, the present setup, constrained by hardware availability, involves a multi-agent simulation executed on a single server, which manages both the simulation environment and the simulated CCAVs, alongside the internal processing of vehicle and infrastructure containers. Consequently, the computational resources to enhance the fidelity and scalability of the arbitration implementation should be increased accordingly to test the solution with different traffic densities. Experiential measurements of this impact reveal that each simulated vehicle increases CPU usage by approximately 10%, GPU usage by approximately 15%, and RAM memory usage by roughly 1 GB. Consequently, the simulation achieves 30 FPS with four vehicles but drops below 10 FPS with more than six, directly impacting its stability and indicating a scalability limitation for future iterations.  
Furthermore, empirical evaluation indicates a performance disparity in the Carla simulator, exhibiting lower frame rates on Linux systems compared to Windows. A major factor is believed to be the poor performance of NVIDIA's Linux drivers in rendering tasks when compared with Windows drivers. For future iterations, current investigations are exploring a distributed simulation architecture, deploying the Carla Server on a Windows instance while concurrently connecting Linux-based clients running the ROS.