esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Influence of Segmented Rollers on Slab Bulge Deformation
## 7 Conclusions  
A 3D finite element thermomechanical coupling model between segmented rollers and a casting slab was established to investigate the influence of the segmented rollers on slab bulge deformation. The traditional rigid continuous casting rollers were replaced by elastic continuous casting rollers in these models, which is an advantage that this model has over traditional models. The main conclusions are as follows:  
(1) The slab bulge deformation under the elastic rollers increased by around 74.3% compared with that of traditional rigid rollers. The results indicated that the stiffness of the casting rollers would have a great influence on slab bulge deformation and the elastic rollers were more suitable for the slab bulge deformation analysis.  
Figure 11: Comparisons of slab bulge deformation with different segment roller structure parameters.  
(2) The slab bulge deformation was 1.43 mm under the elastic solid rollers and was reduced by 10.5% under the two-segment rollers compared with elastic solid rollers, while the slab bulge deformation dropped by 37.1% under the three-segment rollers.  
(3) The results showed that the bulge deformation could be controlled by reducing the roller spacing or increasing the roller diameter. The slab bulge deformation on the wide side increased when the roller spacing increased by 50 mm, with the maximum increase of 67.1% and the minimum increase of 32.9%. The average value of the slab bulge deformation was reduced by about 15% when the roller diameter was increased by 20 mm.  
(4) The bulge deformation on the wide side was reduced by 75.4% when the roller diameter and spacing of the segmented rollers were optimized. This result indicates that the optimized structural parameters of casting segmented rollers could effectively control slab bulge deformation.  
Conceptualization, Q.Q.; methodology, Q.Q.; formal analysis, Q.Q.; investigation, M.L. and J.H.; writing--original draft preparation, M.L.; writing--review and editing, Q.Q.; visualization, M.L.; supervision, Q.Q.; funding acquisition, Q.Q.  
This research was funded by the National Natural Science Foundation of China, grant number: 51375041.  
The authors declare no conflict of interest.
## 4 Discussion  
The curve in Figure 5(b) converges faster and has greater jitter. This is because the packet size of simulation in Figure 5(b) is smaller than that in Figure 5(a). For LC-MARL, the D-TDD configuration policy is easier to learn with less network load. In D-TDD configuration task, the termination condition of each episode is for all UE buffer to be cleared. So, in each training episode, agent reaches the end of episode through fewer training steps, which reduces the number of samples in each episode. Therefore, total system reward of each episode jitter greatly. However, it is observed that total system reward gradually stabilizes as the training progresses and converges at 110 episodes. In other words, the algorithm completes exploration of scenarios with less load through less training.  
As shown in Figure 7, compared to DDRL based framework our proposed control framework performance well on overall system rate in all traffic types. This is because DDRL based scheme is a fully decentralized MARL method, where the learning goal of each BS is to improve service quality of BS rather than overall system rate. Although our solution has the same learning goals as the DDRL based solution, we improve overall benefits of fully distributed MARL method through proposed effective leniency control. In addition, the overall system rate under high load traffic increases slowly, because the system rate gradually reaches the limit of system capacity as the size of the data packet increases under high load traffic.  
As described in Figure 8, the DDRL based method is more likely to fall into local optimum for the absence of effective global information. This is because a sub-optimal policy in joint action space is preferred over an optimal policy for the distributed agents in DDRL based method. This is the relative over-generalization issue in MARL. Compared to DDRL based method our proposed method improves overall system rate by effectively controlling the calculation process of the target Q-value according to Equation (20). Furthermore, auto-encoder improve the ability of leniency control to handle the high dimensional learning environment in D-TDD configuration task. This also enhances the performance of our proposed method. Moreover, as the number of BSs increases, the dimension of the joint action space becomes higher. This leads to a more serious problem of relative over-generalization. Therefore, the performance gain of the proposed method in Table 6 increases with the number of BSs increasing.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Standards
#### 3.1.2 Charging Connector Types  
Various connector types and setups have been examined to ensure a dependable and safe link between the charging station and the electric vehicle. Commonly utilized connectors for DC fast charging include CHAdeMO and CCS [62] connectors [124]. These connectors guarantee compatibility between EVs made by various manufacturers and the charging infrastructure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Standard** & **Description** \\ \hline IEC 60038 & Specifies the standard voltage levels used for electrical power systems and charging applications. \\ \hline IEC 62196 & Standards conductive charging components for connectors, cables, outlets, plugs, inlets, and communication protocols used in AC charging of electric vehicles. \\ \hline IEC 60664-1 & Insulation coordination for equipment within low-voltage systems. \\ \hline IEC 62752 & Provides guidelines for connecting electric vehicles to information and communication technology, ICT, networks. \\ \hline IEC 61851 & Covering various charging modes, communication protocols, and safety features. \\ \hline SAE J1772 & Requirements for the electrical connectors and communication protocols for Level 1 and Level 2 charging used for AC charging of electric vehicles in North America. \\ \hline SAE J2344 & Provides guidelines and test procedures for evaluating the crashworthiness and safety of electric vehicle battery systems. \\ \hline SAE J2894/2 & Requirements for the power quality and conductive charge coupler used in DC fast charging electric vehicles. \\ \hline SAE J2953 & Standards for interoperability to provide guidelines for conductive automated charging systems for electric vehicles. \\ \hline SAE J2847/1 & Communication between vehicles as a distributed energy source and the grid. \\ \hline SAE J3068 & Wireless power transfer for light-duty plug-in/electric vehicles and alignment methodology. \\ \hline SAE J2931/7 & Evaluating the electrical performance of components used in hybrid and electric vehicles. \\ \hline ISO 15118 & Standards for V2G communication protocols and interfaces between vehicle and charging infrastructure. \\ \hline ISO 17409 & Specifications and reliable measurement of energy consumption allow for accurate comparisons and evaluations of different EV models. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Major standards of EV charging systems [11; 21; 26; 38; 60; 77; 92; 95; 121; 123; 124; 125].
## 4 Materials and Methods
### Dynamic Interactions: Insights from Docking and Molecular Dynamics  
The process began with acquiring the protein structures of the IKK\(\alpha\) and NF-kB1 proteins from the PDB database using the PDB IDs 5 EBZ [49] and 1 SVC [50], respectively. Visualization and removal of ligands and small molecules were facilitated using ChimeraX [51]. All ligands were constructed using the GaussView program, and both the ligands and receptors were saved in mol2 format. Subsequently, the ligand was docked with the receptor utilizing the GOLD Software (2022.1) [52]. It is important to note that docking was performed within a defined grid box of 8 \(\times\) 8 \(\times\) 8 dimensions, centered on specific XYZ coordinates for each model. For example, for the IKK\(\alpha\) model (PDB ID: 5 EBZ), the coordinates used were 88.11, 65.64, and 0.37. For the NF-kB1 model (PDB ID: 1 SVC), the coordinates were 34.30, 9.39, and 40.52. From the ensemble of docked conformations, those achieving the highest GOLD fitness score were meticulously selected for further analysis. Following the docking phase, protonation states of the protein-ligand complexes were determined at a physiological pH of 7.4 using the PDB2 PQR server [53]. Subsequently,all-atom Molecular Dynamics (MD) simulations were conducted on IKK\(\alpha\) and NF-kB1 proteins, along with their respective inhibitors, CID16048085 and CID5280961. These simulations were carried out within a periodic boundary using AMBER22 [54]. For force field parameters, the ff19 SB force field [55] was applied to the target proteins, encompassing both bonded and nonbonded interactions. Parameters for the inhibitors (CID16048085 and CID5280961) and spilanthol were generated using the leap module in conjunction with the general AMBER force field 2 (GAFF2) [56]. Furthermore, RESP charges for spilanthol were computed following established standard procedures [57]. To finalize the structural setup of each system, any missing hydrogen atoms were added using the LeaP module. Additionally, Na\({}^{+}\) ions were introduced to neutralize charges. Subsequently, the newly introduced hydrogen atoms underwent an energy minimization process employing steepest descents (SD) and conjugate gradient (CG) algorithms. Finally, the entire protein-ligand complex underwent a complete energy minimization procedure to achieve an optimal state. The complex was then gradually heated from 10 to 310 K under 1 atm of pressure before initiating a 100 ns MD simulation. The MD trajectories were derived from the final 20 ns, and RMSD analysis was performed by CCPTRAJ module for assessment of the stability of the complex simulations [58]. Additionally, the average structure was computed based on data from the final 20 ns of simulation using the Chimera version 1.15 program [59]. Following this, 2D interaction diagrams were generated using the Discovery Studio program.
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 4 Experimental Works and Results
### Experimental Setup
#### 4.1.1 Network Lifetime  
Figure 4 depicts the average network lifetime in minutes for rank, Sybil, and sinkhole attacks. The simulation was run four times with 30, 60, 90, or 120 nodes. We compared the proposed mechanism (MMTM-RPL) with DCTM-RPL [28], RBAM-IoT [29], RPL-MRC [30], and DSH-RPL [25]. The number of nodes is shown on the \(x\)-axis, and the network lifetime is shown in minutes on the \(y\)-axis. For the different numbers of nodes, the network has a longer lifetime with the proposed mechanism than with the other mechanisms. For a rank attack, for example, the average network lifetimes were 0.45, 0.38, and 0.40 min for MMTM-RPL, DCTM-IoT, and RBAM-IoT, respectively. The clustering route optimization also played a crucial role. Overall, the overhead is shared by two layers (e.g., fog and sink). With MMTM-RPL, the message overhead is decreased by using MAs, which reduce the number of redundant messages received and forwarded by intermediate nodes. Thus, less energy is consumed, increasing the residual energy and ultimately increasing the network lifetime. In contrast, the message overhead is not managed effectively by the other mechanisms. In addition, note that the lifetime was higher for sinkhole and Sybil attacks.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation tool & Contiki OS based Cooja 3.0 \\ MAC & CSMA/CA+ MICMAC \\ Transport protocol & IPv6 \\ Topology & Random \\ Node type & Tmote Sky \\ Simulation coverage area & 100 m \(\times\) 100 m \\ No. of nodes & 30–120 \\ No. of malicious nodes & 3–12 \\ Legitimate to malicious node ratio & 1–100 \\ \(R_{x}\) ratio & 30–100\% \\ \(T_{x}\) ratio & 100\% \\ \(T_{x}\) range & 50 m \\ Interference range & 50 m \\ Traffic type rate & Constant bit rate of 6 pkt/min \\ Packet size & 46 bytes \\ Routing protocol & RPL \\ Network protocol & IP based \\ Start delay & 5 s \\ Simulation time & 30–60 min \\ Mobility speed & 0–6.23 km/h \\ Link failure model & UDG with distance \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 4: _Cont._
# Analysis of 'Pre-Fit' Datasets of gLAB by Robust Statistical Techniques
## 2 Structure of the Dataset and Preliminary Analysis  
In this section, our study uses actual and publicly available data collected by a Javad 'TRE_G3 TH' receiver, installed at a permanent station named KIR0 in Sweden. This is a standard geodetic-grade receiver as those usually found in the IGS network. (_IGS data can be downloaded from the URL [[https://www.igs.org/data/--accessed](https://www.igs.org/data/--accessed)]([https://www.igs.org/data/--accessed](https://www.igs.org/data/--accessed)) on 15 May 2021._) The day of the experiment is the day of the year (DoY) 203 of 2018 (i.e., November 4 th). Code pseudorange C1C measurements are logged with a period of 30 s, i.e., the _sampling frequency_ is 1/30 Hz, so we have 2880 _epochs_ for the entire day. Each epoch contains a record, as in Table 1, for each satellite 'visible' and 'tracked' by the receiver. Figure 1 illustrates the dataset which contains 24 h of pre-fit residuals, the output of the gLAB MODELLING module. This dataset contains both the _pseudorange_ data corrected for some inconsistencies by the PREPROCESS module as briefly explained above, and the pseudorange data as predicted by the model. The computation of the pseudorange takes into account a number of model corrections following the SPP [7]. Note that, since we are using a fixed station with known coordinates and the satellite positions can be obtained from the broadcast navigation message of the satellites, we can compute the geometric range, the _measured range_ in Table 1, for each of the tracked satellites. Then, the pre-fit residuals in Equation (3) turn into:  
\[prefit_{rec}^{sat}=c\delta t_{rec}+\varepsilon_{rec}^{sat}\ \ ;\ \ sat=1,\ldots,n \tag{4}\]  
Therefore, we should expect all pre-fit residuals from all satellites around the receiver clock offset value \(c\delta t_{rec}\) to be only distinguishable by the noise of the measurement, \(\varepsilon_{rec}^{sat}\). Under normal conditions, this _pseudorange noise_ reaches one meter at the high elevation of the satellite and few to several meters at low elevations. In Table 1, an excerpt of two epochs from the complete data table of about 32,000 rows was reported; this table contains the satellite identifier and the pre-fit value, the plain difference between _modeled_ and _measured_ range. Note that at the first epoch, 30.00, the order of magnitude of the pre-fit is about 1 meter for all satellites while at epoch 77,520,  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Year** & **DoY** & **Epoch** & **Const.
# A Projection-Based Augmented Reality System for Medical Applications
## 4 Discussion  
Figure 10 shows that performance of the MMOD system in facial features detection was the slowest of the methods tested. However, in terms of accuracy, as shown in Table 1, it was the best performer. Since the speed performance of the MMOD method was acceptable for the five feature points, it was then incorporated into the proposed system. The results of the second set of experiments showed the average positional deviation for the first head phantom, and the total average errors averaged to around 2.18 mm, while for the second head phantom the average error was around 1.95 mm, so the total average for both heads was around 2.065 mm. This value was quite acceptable for medical applications, since [4] shows that 2.5 mm for HMD is acceptable. It is noted that dimming the light resulted in slightly larger errors for both head phantoms, so it is suggested that a projector with high lumens should be used for our proposed system.  
Figure 11: Example of projections of blood vessels with observer at (**a**) 0 degree, (**b**) +30 degrees, (**c**) +60 degrees, (**d**) \(-\)30 degrees, and (**e**) \(-\)60 degrees.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**System** & **P1** & **P2** & **P3** & **P4** & **P5** & **P.Avg.** & **Q1** & **Q2** & **Q3** & **Q4** & **Q Avg.** \\ \hline NDI & 1.34 & 1.81 & 1.62 & 1.55 & 1.83 & 1.63 & 1.53 & 1.62 & 1.67 & 1.69 & 1.630 \\ \hline Proposed & 1.54 & 1.51 & 1.58 & 1.51 & 1.5 & 1.528 & 1.48 & 1.37 & 1.41 & 1.53 & 1.448 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performances of NDI vs. Proposed System on the head phantoms.  
In the last experiment that was performed, which was to compare the existing NDI system with our proposed system, in terms of accuracy, on the phantoms. Even though the experiment was not exhaustive, it did show that, under regular conditions, the proposed system could perform as well as the NDI system.
# The Influence of Steel, Glass and Basalt Fibres on Selected Parameters of Construction Mortars
## 4 Discussion
The differences in strength between individual samples with different fibre contents described above indicate the clear influence of the fibres used and their content on the strength of concrete. While there are differences in the assessment of the increase in bending strength, the vast majority of authors confirm the beneficial effect of fibres on improving this parameter. The increase in this parameter results from the type and amount of fibre introduced. In general, the brittleness of cement mortar in this case is improved by introducing a specific type of fibre. Each type of fibre used in mortar with the same composition produces different bending strength values. In the capillary pull-up test, the series with steel fibres showed the lowest mass increase compared to the other tested series. Compared to the reference series, this decrease was 60.6%. In general, all series with fibre additives showed a lower mass increase in the capillary pull-up test. In the water absorption test, a similar trend was observed, with glass fibre mortar samples showing the lowest mass increase. Compared to the reference series, this decrease was 33.8%. All tested series were characterised by water absorption below 5%. Therefore, they can be used in places exposed to moisture. This is because fibres limit the formation of microcracks and shrinkage cracks, which promote water migration in mortars. In addition, fibres improve the tightness and homogeneity of mortars, which also contributes to reducing water absorption. Laboratory tests are also time-consuming. In order to avoid performing more laboratory tests, relationships between individual parameters for the analysed series can be developed. This allows conclusions to be drawn about other parameters based on the results of a single test, thereby reducing the number of tests required. This article presents such relationships for the analysed series, enabling more effective and economical testing. A statistical analysis was also performed for the results obtained for two parameters: compressive strength and capillary pull-up. Depending on the parameters we want to improve in the mortars, we can select a specific fibre type. Glass fibres primarily reduce the capillary pull-up and water absorption of mortars. If we want to achieve the highest possible flexural strength, we should choose mortars with basalt fibres. Mortars modified with basalt fibres are also less resistant to moisture than mortars modified with glass fibres. In the next stage of our planned research, we will focus on the analysis of the microstructure of mortars. We will discuss the theoretical aspects of the issue under consideration. We will focus on theoretical analyses and model cause-and-effect relationships. These issues will be the subject of further scientific research.
## 5 Conclusions  
Today, the use of additive manufacturing techniques for producing metal components has found a progressively diverse range of applications. Among these techniques, one that stands out for its exceptional ability to fabricate components swiftly and cost-effectively is WAAM. This research delves into one of the primary challenges associated with WAAM, which is the uniformity of weld bead height, and seeks to investigate the influential parameters affecting this geometric parameter comprehensively. A total of 81 experiments were meticulously conducted, encompassing varying levels of welding speed, nozzle angle, wire speed, and voltage parameters.  
To accurately ascertain the weld line's top edge, sophisticated image processing techniques were harnessed. These techniques enabled the precise determination of the weld line's top edge, representing the uniformity of weld bead height. This study scrutinized the acquired line profiles by applying wavelet transform. This analytical approach allowed for a thorough assessment of the uniformity of weld bead height. Key findings regarding height uniformity according to experimental results are listed as follows:  
* The smoothest welding line was obtained at the highest voltage value in the selected range.
* The most uniform welding was achieved by setting the nozzle in a 90\({}^{\circ}\) position to the welding path.
* Increasing the wire feed rate improved the quality of the welding line in terms of height uniformity.  
Figure 16: Two turbine blades manufactured: (**A**) front view; (**B**) top view.  
This rigorous experimentation and analysis revealed an optimal configuration for manufacturing a sample component. This configuration proved instrumental in producing a component that met satisfactory quality standards. Furthermore, significant emphasis was placed on assessing the repeatability of the component production process, ensuring consistency and reliability in the manufacturing process.  
A.F.; Methodology, Supervision, Formal analysis, Writing, S.K.; Writing--Review and Editing. H.T.; Data curation, investigation, Formal analysis, Writing--original draft. S.M.E.; Writing--Review and Editing, Validation. H.A.; Writing, Validation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.