esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This was further shown in [PERSON] et al. [37] which modelled experiments from [PERSON] et al. [15]. The combined use of SPH and DEM clearly resulted in negligible variations of debris dynamics when changing the kinematic friction coefficient \(f_{\mathrm{c}}\) of the materials. Additionally, DEM implementation was also prone to numerical instabilities, also resulting in high computation times. In order to address the aforementioned issues, the newly implemented DualSPHysics-CHRONO coupling in DualSPHysics v5.0 [38; 39] is used to model the container trajectories measured in [PERSON] et al. [21; 22]. This is, additionally, a first necessary step to study the feasibility of such approach and successively extending their use for flow-debris-structure interactions. DualSPHysics is chosen due to its advantage over other SPH numerical models of using the computational power of the Graphics Processing Unit (GPU) of a PC to speed up simulation times up to 146 times [38]. CHRONO [40; 41] is a multi-physics engine that allows to solve rigid or deformable contacts and impacts between solids. In addition, it includes the possibility of integrating different types of constraints as recently done by [PERSON] et al. [42] to simulate a planing hull. In contrast to the DEM implementation, CHRONO allows for a realistic frictional behaviour due to the inclusion of a full Coulomb sliding/sticking/rolling model. Additionally, the application of different types of constraints such as joints, hinges and springs allows to include complex structures allowing to realistically model the ones found in coastal regions or in harbours. Another advantage is the increased numerical stability gained when compared with DEM coupling. The main objective of this work is to validate the coupling of DualSPHysics and CHRONO with the experiments presented in [PERSON] et al. [22]. First, the modelling of the hydrodynamics of the flow is validated, then a sensitivity analysis of the modelling of the details of the debris geometry and description of the material characteristics on the debris dynamics is carried out. Finally, the results of the calibrated models are presented and discussed. The remainder of the manuscript is organized as follows. The explanation of the methodology, illustrating the numerical setup along with a brief introduction on the numerical methodology used is presented in Section 2. In Section 3 the main results of the initial hydrodynamics validation and subsequent calibration and validation of the debris dynamics are presented. Finally, in Section 4, the results are analysed, and the feasibility of this numerical approach is discussed in relation to potential applications for debris-structure interactions and conclusions of the work are given.
# Solving Optimal Electric Vehicle Charger Deployment Problem
## 6 Case Study 2: Region with High EV Density
### Results and Discussions
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Setting Condition** & **Buffer Size** & **Consistency** \\ \hline Baseline & Public DC fast chargers and random point & 1.13 km & 0.7580 \\ \hline a. Charging Station Type & Public level-2 chargers and random point & 1.13 km & 0.7678 \\ \cline{2-4}  & Public DC fast chargers and public level-2 chargers and random point & 1.13 km & 0.6284 \\ \hline b. Uniform Distribution of Reference Data & Center point of grid data & 1.13 km & 0.7649 \\ \hline  & 700 m & 0.8176 \\ \cline{2-4}  & 600 m & 0.8355 \\ \cline{2-4}  & 500 m & 0.8611 \\ \cline{2-4}  & 400 m & 0.8743 \\ \cline{2-4} c. Buffer size & Public DC fast chargers and random point & 300 m & 0.9003 \\ \cline{2-4}  & 200 m & 0.9182 \\ \cline{2-4}  & 150 m & 0.9348 \\ \cline{2-4}  & 125 m & 0.9395 \\ \cline{2-4}  & 100 m & 0.9293 \\ \cline{2-4}  & 50 m & 0.8969 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Setting conditions and results of learning by condition. Machine learning was performed using the selected 500 m size buffer, and each importance is measured through the result of learning and used in the final location selection. Table 5 illustrates the importance of each calculated data. To make the final selection, the ranking is calculated by measuring the score based on variable importance and the data included in the buffer. The reference point for creating buffers to be used in the final location selection is the center point of the 250 m \(\times\) 250 m grid to proceed with site selection for the entire Jeju Island. To calculate location ranking, normalize the data and measure the ranking based on the sum of the normalized data multiplied by the variable importance. If the values are the same, they are calculated in the same rank. Figure 10a shows the ranking of demand for public DC fast chargers across the entire Jeju Island based on the importance of each variable obtained from the machine learning results. Figure 10b presents the current DC fast charger locations added to the location rank distribution. \begin{table}
\begin{tabular}{c c} \hline
**Data** & **Variable Importance [\%]** \\ \hline POI & 16.9264 \\ \hline Surface & 12.9745 \\ \hline Building0 & 11.3435 \\ \hline Work\_Population & 9.4463 \\ \hline Building3 & 8.1137 \\ \hline Traffic & 7.3983 \\ \hline Building1 & 6.768 \\ \hline Flow\_Population & 5.7931 \\ \hline Car & 5.4492 \\ \hline EV\_Car & 5.3769 \\ \hline Parking & 4.2025 \\ \hline Tour & 3.563 \\ \hline Building2 & 2.6447 \\ \hline \end{tabular}
\end{table} 
Table 5: Variable importance. Figure 9: Area with 125 m (blue) and 500 m (red) buffers. It shows that the current public DC fast charger installments and the high-ranking red and orange points generally match. Based on the above results, among the red points, which are the highest-ranking group, areas that do not overlap with the white points where public DC fast chargers are installed can be considered as target areas for additional installation of new public DC fast chargers.
## 3 Results and Discussion
### Discussion  
The fact that the Pockels coefficient of interfacial water on the Ag electrode (5 pm/V) is nearly two orders of magnitude smaller than that on the ITO transparent electrode (200 pm/V) is consistent with the previous report of the Pockels coefficient on ITO and Pt electrodes [8]. Pt and Ag are common in that both are noble metals and are robust against oxidation. These results are consistent with the hypothesis that hydrogen-bonding interactions between solvent molecules and electrode surfaces are effective in the process [8] as well as the dependence on the electrode material, where the Pockels coefficient of GaN electrode is about 60 pm/V [6] comparable with ITO, and the dependence on the solvent type, where methanol and ethanol, which are protonic solvents, have Pockels coefficients comparable to water on the ITO surface [7]. For further verification of the hypothesis, it would be interesting to study the Pockels coefficient at the interface of base metals, on which an oxide film is formed. If the sign of the Pockels coefficient of interfacial water on Ag or Pt is opposite to that on ITO or GaN, it will be essentially connected to the mechanism.
## 1 Introduction
The results show that by preserving residual energy and avoiding overheated nodes as forwarders, high throughput is achieved with the ultimate increase of the network lifetime. Moreover, the composer rest-server is used to create the RESTful API to trigger the application-specific services provided by the blockchain network. The access control rules offered by the smart contract are used to authenticate and authorized the users and information related to patients and other medical data, e.g., only the authorized medical personnel are allowed to manipulate and access the patient medical information. The traditional database is replaced by the CouchDB to overcome the issue of redundancy and support large transactions of data. Therefore, each peer in the network is equipped with CouchDB to support file storage within the entire blockchain network. Castalia [16], a simulation tool, is used to evaluate the performance of the proposedprotocol, and its comparison is made with Multipath Ring Routing Protocol (MRRP), thermal-aware routing algorithm (TARA), and Shortest-Hop (SHR). Likewise, Hyperledger Caliper [17] is used to evaluate the performance of the blockchain system in terms of transaction throughput and latency. Evaluation results illustrate that the proposed protocol performs significantly better in balancing of temperature (to avoid damaging heat effect of the body tissues) and energy consumption (to prevent the replacement of battery and to increase the embedded sensor node life). The remaining of this paper is categorized as follows: Section 2 gives the brief on WBAN approaches and gives the details insight on related projects and their limitations. Section 3 explains the thermal/energy-aware routing protocol based on blockchain for secure monitoring of patient vital sign. Section 4 presents the simulation and performance evaluation of the proposed blockchain-enabled routing protocol. Section 5 concludes the paper with future research directions.
## 7 Concluding Remarks  
The work carried out during the MASSIVE project focused on the definition of new waveforms based on the features of the ME-SSA protocol (QPSK modulation and regular time-domain pilot symbol insertion). The work also led to the implementation and testing of quasi-constant envelope spread spectrum signals as well as the implementation of the MSD algorithm in a real-time ME-SSA receiver.  
The ME-SSA receiver implementation covered the following important aspects:  
* The PS, resilient to non-GEO scenario environments, and its adaptive threshold mechanism to better handle MDs and FAs in the case of an MSD demodulator,
* The single stages of MSD, which is the most resource demanding block of the receiver chain, in order to optimize their behavior as much as possible, trying not to increase the complexity beyond measure, while remaining adherent to the specifications,
* The weight calculation block, in order to find and implement the algorithm to calculate the weights to best approximate MMSE in the MSD approach.  
The real-time implementation of algorithms revealed many challenges to reach the target performance results obtained by the software simulation in [9]. In particular, implementation issues of the real-time preamble search and associate misdetection and false alarms were investigated in depth.  
To this end, the logic of some heritage blocks of the demodulation chain was not optimized to match with MSD requirements at the beginning, and many optimizations were performed on algorithms and constituent blocks of the designed receiver. For instance, the logic of the scheduling mechanism used to feed the demodulator with detected data chunks was modified from a \"dispatch if not empty\" to a \"dispatch if full\" logic.  
As a concluding remark of the undertaken project, it is confirmed that the adoption of the ME-SSA solution at the receiver, based on evolved PS, MSD and SIC sub-blocks, increases the spectral efficiency or aggregated throughput, compared to that of the E-SSA receiver. This is achieved at the cost of an increased complexity, a higher resource demand and latency (especially for the waveforms with a higher spreading factor, e.g., 128 and 256).  
Overall, a trade-off between the spectral efficiency improvement and the drawbacks may depend on the use case and system parameters to be considered. It is also confirmed that the power spreading of the received packets has a positive impact on the spectral efficiency improvement even in the case of an MSD-based receiver.  
In conclusion, the implementation of the ME-SSA solution has shown that it is possible to achieve spectral efficiency gain with reference to the E-SSA solution. This translates into the possibility of accommodating a higher traffic load and number of uncoordinated terminals that can be managed by a satellite gateway.
## 3 Results and Discussion
### Dynamic Runs  
Figure 8 reports the breakthrough curves for all investigated SMNZs. From the inspection of the plotted data, it is evident that all samples have a similar performance up to breakthrough, being able to treat about 20-30 bed volumes. In particular, the best and worst results were obtained for CLI-Br (30 bed volumes, 610 mL) and CLI-CI (22 bed volumes, 440 mL), respectively, while PHI-based samples were quite insensitive to their anionic form (about 25 bed volumes, corresponding to about 490 mL).  
Figure 7: As adsorption isotherms and best fitting Langmuir (**A**), Sips (**B**) and Toth (**C**) curves.  
Besides the treated volume up to breakthrough, the curves show different slopes, which indicate different mass transfer zones. Furthermore, in some case, a change in the curve slope is clear, especially in the case of PHI-based samples: such strange behavior can be due to another active phase (chabazite, analcime) which exchanges As, but at a slower rate.  
To obtain a better estimation of the mass transfer zone, the calculation was performed on the curve data ranging from the breakthrough point up to the change of slope. Results are reported in Table 5: the PHI-based samples showed the shortest MTZ, while CLI-based the longest one.  
Such evidence could be due to a faster diffusion of the As ions on the particles surface, allowed by a more dense micelle structure, as previously seen. Furthermore, the different amount of As (V) removed, if compared with the batch kinetic tests, could be due to the high elution rate selected for the present runs, higher than the diffusion rate and the exchange kinetics; thus not giving time to fully exploit the AEC of the tested SMNZs. The obtained values of the Thomas model k constant (see Table 6) are consistent with this hypothesis, being higher for PHI-based samples.  
Figure 8: As breakthrough curves for PHI-Br (**A**), PHI-Cl (**B**), CLI-Br (**C**) and CLI-Cl (**D**) samples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **MTZ, mm** & **M-AEC, mg g\({}^{-1}\)** & **WEC, mg g\({}^{-1}\)** & **AEC, mg g\({}^{-1}\)** & **S** & **E** \\ \hline CLI-Br & 209.12 & 1.12 & 0.73 & 10.64 & 0.11 & 0.07 \\ CLI-Cl & 234.28 & 0.95 & 0.53 & 5.32 & 0.18 & 0.10 \\ PHI-Br & 161.7 & 0.79 & 0.53 & 8.62 & 0.09 & 0.06 \\ PHI-Cl & 141.04 & 1.03 & 0.66 & 4.35 & 0.24 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dynamic parameters from breakthrough curves.  
Among the four samples, PHI-Cl showed the best performances in terms of selectivity (0.24) and efficiency (0.15, Table 5): such values, although quite low, were obtained under severe conditions, and should be considered only for the sake of comparison among the four samples. The q values obtained from the Thomas model are also in very good agreement with the M-AEC obtained with the Michaels approach, except for the PHI-Cl sample, which suffers from a noticeable change in slope.
## 2 Materials and Methods
### The Joule-Thomson Effect  
The restricted flow of a liquid or gas due to encountering an obstacle, such as an expansion valve, orifice, or porous plug, will cause the gas to experience a pressure drop. Since the transfer of external heat and the changes in kinetic energy are usually negligible, and because there is no transfer of mechanical work to and from the liquid or gas flow, the resulting expansion can be considered an isenthalpic process. An ideal liquid or gas will therefore experience a temperature change. This fundamental phenomenon is called the Joule-Thomson effect (denoted J-T), and it allows the obtaining of very low temperature levels through the isenthalpic expansion of liquids or gases.  
The effect of temperature variation for an isenthalpic pressure change is represented by the Joule-Thomson coefficient, defined by Equation (4):  
\[\mu_{J-T}=\left(\frac{\Delta T}{\Delta P}\right)_{h}, \tag{4}\]  
having these variables' meanings:  
* \(\mu_{J-T}\)--Joule-Thomson coefficient;
* \(\Delta T\)--Temperature variation;
* \(\Delta P\)--Pressure variation.  
The Joule-Thomson coefficient equals the isenthalpic lines' slope, as shown in Figure 6. In the region where the J-T coefficient is less than zero, the isenthalpic expansion of the gas or liquid (negative pressure variation) increases temperature, while in the region where the J-T coefficient is greater than zero, that expansion results in a decrease in temperature. The curve that separates the two regions is called the inversion curve. The Joule-Thomson coefficient is zero along the inversion curve because the slope of the isenthalpic line is zero.  
Due to the absence of intermolecular forces, an ideal gas will have its J-T coefficient null compared to a real gas that can have a positive, negative or null coefficient.
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 3 Results and Discussion
### Data Uncertainty  
The main point of uncertainty that may arise in our proposed approach, that is shared with all related works using the DDI corpus, is input-dependent data uncertainty. In this case, the observation noise varies based on the input and is commonly introduced during the data generation process [36]. In order to address this issue, we attempt to deal with the observed inconsistencies during the the pre-processing stage.  
Initially, simple entity name transformations are applied by changing the plural form to the singular form when the same substance is referenced and both cases are found in the Drug_Class set (e.g., \"penicillin\" and \"penicillins\"). Consequently, this leads to a total of 122 cases of identified cases.  
An additional point of uncertainty concerns the classification of drugs into the four classes as each drug mention generally belongs to a single class. However, cases were observed where the same drug was labeled with different classes throughout the instances found in the dataset. Although the percentage of entities in which this is observed is relatively small, the drug mentions in question participate in a large number of interaction pairs. Specifically, 6023 pairs are identified in which at least one entity with multiple types is found.  
Thus, the management of differentiation is sought without rejecting them. For example, in the case of \"corticosteroid\" where the vast majority of occurrences belong to a specific class, the assumption can be made that all instances should be labeled based on the majority class. In contrast, in the case of \"tetracycline\", where the class distribution is not clearly in favor of a single class, no such assumption can be made without introducing more uncertainty in the dataset.  
The class of a drug entity is defined by a <drug-name, rdf:type, drug-class> triplet, where the object takes its value from the set of the four positive drug classes which should be unique for each instance of the Drug_Class class. However, a small amount of drug names cannot be classified to a single class. Moreover, each individual case is characterized by the name of the drug, as well as the set of the additional classes it was labeled in its various occurrences in the dataset. Therefore, for each one of these classes \(c\), a blank node <_:substance-name_c> and a statement in the form of <_:substance-name_c, rdf:type, <> are included in the KG. Furthermore, in order to emphasize that those individual entities are not independent, the property name is defined which participates in <_:substance-name_c, name, substance-name> triplets. Therefore, entities that share the same value in this attribute are referring to the same substance.  
An example of how our proposed approach performs on instances that contain uncertainties can be seen in the example sentence S2 (Table 4) in Section 3.8.
## 4 Experiment and Result Analysis
### Discussion
#### 4.7.2 Method Characteristics under Different Network Connectivity  
When the distributed UAV swarm is running, each UAV needs to perform regular state synchronization and event-triggered task assignment negotiation with UAVs within \(n_{c}\) hops, which also means that each UAV needs to process information from other UAVs within \(n_{c}\) hops. When the mission area is relatively scattered and the connectivity of the UAV network is low, the use of a limited range of autonomous collaboration can reduce the consumption of the network and reduce the computational power consumption of each UAV. While improving the reliability of the cluster, the distributed architecture can also enhance the scalability of the cluster.  
However, when the UAVs are concentrated, such as when the network is fully connected in extreme cases, adopting a distributed architecture will reduce the operating efficiency of the system. The fully connected network requires each UAV to process the information of the entire battlefield, which indicates that each UAV should be equipped with high-performance computing resources. Moreover, the additional negotiation communication required by the distributed architecture will also increase the consumption of the network.  
Therefore, the proposed distributed approach should be combined with centralized control when applied, and dynamically switch between the two according to the network connectivity status.
## 2 Methods
### CFD Method
#### 2.1.4 Mesh Delineation and Mesh-Independence Verification  
In the computational fluid dynamics solution process, the impingement jet model utilizes the grid-blocking method for mesh division. The jet region is subdivided into key areas based on specific change rules, while the remaining grid areas are kept coarse. This approach considers the mesh requirements of different regions, reduces the total number of grid nodes, and ultimately enhances computational accuracy and efficiency. The primary regions of the jet, including the surface of the track tooth, the nozzle wall, and additional components, are subjected to mesh refinement. Table 1 displays the parameters of the model with varying mesh densities.  
To validate the simulation results, the injection pressure is kept at 1 MPa with all other initial conditions unchanged. The same boundary conditions are applied, focusing on the nozzle's axial linear velocity (Velocity) and the tracked tooth pressure (Dynamic Pressure) for analysis [28]. The results are presented in Figure 2.  
The trends of axial velocity and pressure distributions for different grid sizes are consistent, as shown in Figure 4. To maintain accuracy and computational efficiency, 678,022 meshes were employed in the three-dimensional grid model for the jet calculation [28].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mesh** & **Mesh Size (mm)** & **Velocity (m/s)** & **Dynamic Pressure (Pa)** \\ \hline Mesh 1 & 1 & 45.21 & 1,020,234.4 \\ Mesh 2 & 2 & 45.22 & 1,020,450.1 \\ Mesh 3 & 4 & 45.3 & 1,024,043.9 \\ Mesh 4 & 8 & 45.22 & 1,020,485.9 \\ Mesh 5 & 16 & 44.97 & 1,009,342.6 \\ Mesh 6 & 32 & 43.47 & 943,026.4 \\ Mesh 7 & 64 & 43 & 923,008.8 \\ \hline \end{tabular}
\end{table}
Table 1: Verification of grid sensitivity.  
Figure 3: Comparison of simulation-predicted and theoretically calculated jet velocities.