esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Single Satellite Pair System Engineering Requirements
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **DCT Thruster Requirements** & **FCT Thruster Requirements** \\ \hline \multirow{3}{*}{Thrust range} & 0.6 mN \(\leq\) thrust \(\leq\) 8 mN (dynamic) (*) & 50 \(\upmu\)N \(\leq\) thrust \(\leq\) 1 mN (threshold) \\  & 1 mN \(\leq\) thrust \(\leq\) 15 mN & 1 \(\upmu\)N \(\leq\) thrust \(\leq\) 2 mN (target) \\  & (steady-state) (*) & 1 \(\upmu\)N \(\leq\) thrust \(\leq\) 2 mN (target) \\ \hline \multirow{3}{*}{Thrust increment (over the dynamic range)} & \(\leq\)12 \(\upmu\)N & 0.5 \(\upmu\)N \\ \hline \multirow{3}{*}{Total impulse per thruster} & \(\geq\)170 kN-s accumulated as: & \\  & \(\bullet\) & \(<\)140 kN-s in dynamic mode & \(\geq\)60 kN-s \\ \cline{1-1}  & \(\bullet\) & 30 kN-s in steady-state (at & \\ \cline{1-1}  & maximum thrust) & \\ \hline EOL performance degradation & \(<\)20\% & \(<\)20\% \\ \hline \multirow{3}{*}{Thrust noise} & 0.4 mN/\(\sqrt{\upmu}\)Hz for f \(<\) 2.8 mHz & \(\leq\) 100 \(\upmu\)N/\(\sqrt{\upmu}\)Hz f \(<\) 0.3 mHz \\  & _Linear between 2.8 mHz_ & _Linear between 0.3 mHz_ \\  & _and 2.8 Hz_ & _and 0.03 Hz_ \\  & 3 \(\upmu\)N/\(\sqrt{\upmu}\)Hz for f \(>\) 2.8 Hz & \(\leq\)1 \(\upmu\)N/\(\sqrt{\upmu}\)Hz for f \(>\) 0.03 Hz \\ \hline \multirow{3}{*}{Specific impulse} & \(\geq\)2000 s @ 1 mN (\(\blacktriangle\)) & \multirow{3}{*}{\(\geq\)350 s @ 50 \(\upmu\)N} \\  & \(\geq\)2320 s @ 3 mN (\(\blacktriangle\)) & \(>\)3200 s @ 1000 \(\upmu\)N \\  & \(\geq\)2800 s @ 6 mN (\(\blacktriangle\)) & \\ \hline Thruster plume divergence (half-angle & \(<\)15\({}^{\circ}\) & \(<\)15\({}^{\circ}\) \\ cone comprising 95\% of ion flux) & \(<\)60 ms & \\ \hline Response time to reach 63\% of & \(<\)60 ms & \(<\)300 ms \\ \hline \multirow{3}{*}{Slew rate} & 0.03 to \(\geq\) 0.56 mN/s (dynamic) & \multirow{3}{*}{\(\geq\)0.5 mN/s} \\  & \(\geq\)1 mN/s (steady-state) & \\ \hline Thrust vector stability & \(<\)0.2\({}^{\circ}\) (3\(\sigma\)) half cone & \(<\)0.2\({}^{\circ}\) (3\(\sigma\)) half cone \\ \hline Linearity & N/A & \(<\)2\% \\ \hline Update command rate & 10 Hz & 10 Hz \\ \hline \multirow{3}{*}{Specific power} & \(<\)36 W @ 1 mN (\(\blacktriangle\)) & \multirow{3}{*}{\(<\)15 W @ 50 \(\upmu\)N} \\  & \(<\)160 W @ 5 mN (\(\blacktriangle\)) & \\ \cline{1-1}  & \(<\)270 W @ 10 mN (\(\blacktriangle\)) & \\ \hline \hline \end{tabular} (*) The DCT thruster will be required to operate in two distinct modes, referred to as dynamic (“drag-free” operations) and steady-state (relatively coarse thrust control for orbit raising, collision avoidance, etc.). (\(\blacktriangle\)) Significant improvements of the GOCE performance characteristics towards these values are challenging but very advantageous.  
\end{table} 
Table 8: Envelope requirements for the DFAOCS thrusters.
## 5 Results of NSGA-II Optimization Approach  
As shown in Table 6, the non-dominated set of solutions was generated after executing 200 generations in an optimized NSGA-II with population size = 50, crossover rate = 0.4, and mutation rate = 0.6. The optimal solution among them was identified using the weighted sum technique. The optimal choice was determined as the solution with the smallest preference index (PI), since the problem was solved using the minimization approach. The penultimate solution was the optimum, i.e., the PI value was 0.16549756, duration of implementation was 274 days and cost was USD 550,000. The solutions obtained after executing 200 generations using the optimization algorithm are presented in Figure 7. The curve illustrates the relationship between cost and time, representing the non-dominated set of solutions that reflects the best achievable balance between these two factors. This set comprises the most efficient solutions, where no single criterion can be improved without compromising the other. Table 7 represents the appropriate implementation mode for each of the activities in the project; it illustrates the execution mode for each project activity after applying the optimal solution identified through the optimization algorithm. This selection was made by achieving the optimal balance between cost, time, and resources based on the optimization criteria adopted in this study.  
Based on Table 6, the selected execution mode for each project activity was determined. Table 8 presents the optimal duration, cost, and resource allocation for each project activity. Furthermore, Figure 8 presents the project network, illustrating the execution time for each activity, the earliest start and finish times, the latest start and finish times, as well as the floating period duration.  
\begin{table}
\begin{tabular}{l c} \hline Indirect cost (USD) & 300 \\ Direct cost & 575,000 \\ Contractual duration (day) & 285 \\ Acquiring costs (USD) & 250 \\ Releasing costs (USD) & 250 \\ Delay penalty (USD/day) & 500 \\ Available resources (day) & 9 \\ Alpha cut & 0.5 \\ \hline \end{tabular}
\end{table}
Table 5: Parameters of project.  
used for scheduling optimization is the implementation of the NSGA-II algorithm and its capability of reassigning resources for noncritical activities within their floating periods. This results in effective monitoring of constraints to resource without exceeding cost or duration of project activities.
# Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review
## 3 Background
### Ontology/Schema Development  
In general, an ontology can be described as a formal definition of the concepts and their relations over a given domain, ranging from simple vocabularies to complex logic-based formalisms. In the context of knowledge graphs, the term is closely related to the schema of the knowledge graph, which can be described using an ontology. In the context of this paper, we use ontology, vocabulary, and schema interchangeably. Ontologies can be created either by defining concepts and relations through domain analysis, or through analysing the available data [2]. These are often referred to as the top-down and bottom-up approaches, respectively [37]. The top-down approach is traditionally done manually, while the bottom-up approach can be done automatically and semi-automatically.  
Regarding the top-down approach, it usually starts with obtaining an overview of the given domain through reading papers and books, interviewing experts, etc. The knowledge from this phase is then used to further formalise the ontology, until a specification of the ontology is obtained. The task of formalising a given domain may be substantial, depending on the domain to be modelled. An agile methodology is therefore often employed [38]. This can be obtained using competency questions throughout the development process [39], meaning that the ontology is incrementally developed by adding concepts based on questions the ontology has to answer. Other methods include using ontology design patterns [40], which enable the reuse of existing ontology designs and modelling templates. Regarding the bottom-up approach, automatic and semi-automatic approaches are often used to extract information from the given input data in order to model the ontology. These methods also relate to the approaches for automatically integrating data into a knowledge graph. The techniques may involve measuring the relevancy of entities in the data, based on count, or relations, through patterns [41].  
It is also possible to combine the two approaches by using some information from the top-down approach as a basis for the further development of an ontology with the bottom-up approach, which is often described as the middle-out approach [37]. Such approaches allow validation of intermediate results, similarly to the bottom-up approach [2].
## 3 Case Studies
### Overview of Installation Sites  
#### 3.1.1 Solar Thermal System at IIT Roorkee  
The solar thermal system (Figure 2) consists of a parabolic solar concentrator, a heat exchanger, and thermal fluid, which is innovatively designed to harness thermal energy during daylight hours and store it for heating purposes when needed.
## 4 Results and Discussion
### Modelling Results  
#### 4.2.1 Hyperparameter Tuning  
By using BAS and 10-fold CV, the optimal hyperparameters can be obtained from the fold having the minimum \(RMSE\). As shown in Figure 11, the first and fourth folds achieved the minimum \(RMSE\) in the process of FS and ASR modelling. The \(RMSE\) converged during the 50 iterations, illustrating the success of hyperparameter tuning and model establishment. Finally, the optimal hyperparameters chosen for FS and ASR datasets were both \(minNumleaf=1\) and \(numTree=87\).  
Figure 10: SEM micrographs of (**a**) 75 \(\upmu\)m WGP, (**b**) mortar-75G10, (**c**) pretreated 75 \(\upmu\)m WGP by 2% alkali and 2% Na\({}_{2}\)SO\({}_{4}\), and (**d**) mortar-75G10S2H2.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 6 Network Data Analytics Function (NWDAF)
### Challenges of Federated Learning
#### 6.2.4 Statistical Heterogeneity  
With varying data from the involved clients, the distribution is usually non-identical. This is because the clients capture or generate data in a non-identically distributed fashion across the domain. For example, in eHealth settings, the data captured via wearable devices vary from individual to individual. In autonomous driving applications, the vehicles that are from different automobile makers and differently digitized will capture in a non-identically distributed manner. Furthermore, the number of data points for different devices may significantly vary, which negates the independent and identically distributed (I.I.D.) assumptions usually used in distributed optimization. It also increases the probability of stragglers and complexity.
## 9 Efficiency Analysis
### Comparison and Analysis of Security Properties of Several Existing Schemes
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Scheme** & **Immutable** & **Integrity** & **Nonrepudiation** & **Reliability** & **Traceability** & **Synchronicity** & **Confidentiality** & **Efficacy** \\ \hline IP protocol & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & Highest \\ IP protocol with signature 1 & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & Lowest \\ AH protocol & ✓ & ✓ & ✗ & Higher & ✗ & ✗ & ✗ & Higher \\ ESP protocol & ✓ & ✓ & ✗ & Higher & ✗ & ✗ & ✓ & Higher \\ [11] & ✗ & ✓ & ✓ & ✗ & ✗ & ✗ & Medium \\ [12] & ✗ & ✓ & ✓ & ✗ & ✗ & ✗ & Higher \\ [13] & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & Medium \\ [15] & ✗ & ✓ & ✓ & ✗ & ✗ & ✓ & Lower \\ [16] & ✓ & ✓ & ✓ & Higher & ✗ & ✓ & Low \\ MHC & ✓ & ✓ & ✓ & High & ✓ & ✓ & Higher \\ \hline \hline \end{tabular}  
* 1 Sign and authenticate IP datagram packet by packet.  
\end{table} 
Table 5: Comparison of security properties of different schemes.
## 2 Materials and Methods  
CaWO\({}_{4}\) single crystals doped with various concentrations of Nd (0.1, 0.5, and 1 at%) were grown with the floating zone (FZ) method. First, starting materials: CaO (Furuuchi Chemical, 99.99%), WO\({}_{2}\) (Rare Metallic, 99.9%), and Nd\({}_{2}\)O\({}_{3}\) (Rare Metallic, 99.99%) were mixed and sintered at 600 \({}^{\circ}\)C for 8 h. After that, they molded into rod-shaped and sintered at 1200 \({}^{\circ}\)C for 8 h. And then, polycrystalline rods were obtained. By using the rods, we conducted the crystal growth with the FZ furnace (Canon Machinery, FZD0192) having dual Halogen lamps. The condition of the rotation rate and the pulling-down rate were 14 rpm and 5 mm/h. To confirm the crystalline phases of the present crystals, powder X-ray diffraction (XRD) measurements were conducted with a diffractometer (Rigaku, MiniFlex600).  
The measurement of diffuse transmission spectra was performed with a spectrophotometer (Shimadzu, SolidSpec-3700) as a basic optical property. The tested ranges were 270-1600 nm. Both a PMT and an InGaAs-based detector were mounted in the spectrophotometer, and they respectively monitored at 270-870 nm and 870-1600 nm by different photodetectors. In the evaluation of photoluminescence (PL) properties, PL excitation and emission contour maps were measured with a Quantaurus-QY Plus (Hamamatsu, C11367). The excitation and monitored wavelengths were 250-850 nm and 195-1675 nm, respectively. PL decay curves were measured with a Quantaurus-\(\tau\) (Hamamatsu, C13534).  
As radiation-induced luminescence properties, X-ray-induced scintillation spectra, decay curves, afterglow curves, and dose rate response properties were evaluated by our original setups [29; 30; 31]. In the measurement of dose rate response properties, the NIR-scintillation light from the samples was detected by an InGaAs-photodiode (PD) (Hamamatsu, G12180-250A). The light was carried via a fiber (Thorlabs, FP600 ERT) of 5 m in length and 600 \(\upmu\)m \(\varphi\), and it simulated actual applications. The signals from the PD were analyzed as currents using a picoammeter (Keysight, B2985A).
## 5 Numerical Investigation
### Simulation Results  
Numerical results for the truss girder-bolted deck plates are evaluated based on the test results described previously. Table 5 and Figure 19 enable a comparison of deflection between numerical calculations and test results with respect to the specified construction loads. The numerically-computed deflection for both 135-ED1-3.2A and B is 22.5 mm, which is fairly similar to the two measured deflections, as shown in Table 5. This is similar for other numerical results. Moreover, all measured and numerical deflections are smaller than the design deflections defined previously.  
Figure 19 shows distributed loads as a function of deflection for the three types of numerical model. It is seen that all numerical models lead to the deflections varying the distributed load,  
Figure 19: Distributed loads as a function of deflection between numerical predictions and test results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimen ID**} & **Measured Construction** & **Measured** & **Numerical** & **Design Deflection** \\  & **Load (kN/m\({}^{2}\))** & **Deflection (mm)** & **Deflection (mm)** & **(mm)** \\ \hline
135-T1-3.2A & 4.38 & 20.5 & 22.5 & 25.5 \\
135-T1-3.2B & 4.38 & 23.0 & 25.5 \\
150-T3-3.8A & 4.70 & 27.3 & 23.0 & 28.5 \\
150-T3-3.8B & 4.70 & 24.4 & & 28.5 \\
200-T5-4.5A & 5.90 & 26.1 & & 32.0 \\
200-T5-4.5A & 5.90 & 22.4 & & 32.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of deflection between numerical predictions and test results.  
reasonably similar to the test results. Some differences are, however, observed in the initial slopes for most specimens, that is, the initial slopes tend to be greater than those for numerical models. This is because the specimens were somewhat unstable in the early stage of the tests, noting that the specimens were simply supported without any constraints at both ends. The slopes stabilize after a particular level of load is reached. After the specified construction loads are reached, the numerically-calculated deflections, especially for 200-ED5-4.5, tend to diverge from the measured deflections, which is attributed to the perfectly elastic-plastic behavior defined for all steel members. Nonetheless, this difference is insignificant in this study because the range of interest in the numerical simulations is the region below the specified construction load, and within this range, the results are sufficiently comparable.  
Figure 20 shows distributions of von Mises stress and axial stress generated from numerical simulations for deck plate and truss girder of 135-ED1-3.2 for the construction load of 4.38 kN/m\({}^{2}\) used for evaluation of deflection. It is observed that stresses for both deck plate and truss girder are localized near the supports. This stress localization along with the failure modes of test specimens is discussed in detail later in this paper.
# Experimental and Numerical Predictions of Cryogenic Leakages in Welded Steel Plates
## 3 Numerical Preparations
### Numerical Validation  
This work measured residual stress after welding and compared the results with the results obtained using the finite element modeling (FEM) approach. Figure 10a compares the results of XRD with the surface transverse stress. Additionally, the Z-directional distortion is also measured and compared between the numerical simulation and the experiment, as shown in Figure 10a. Both comparisons illustrate that the FEA welding analysis reflects the experiment.
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
## 2 Proposed Method  
### Main Ideas  
The proposed LTE networks utilizing the downlink scheduling are constructed for improved coverage and infrastructure. It also supports the automatic connection through machine-to-machine communication. Shared spectrum models are used to allow for building the integrated networks that are connected with Industry 4.0 through LTE models. ITU has established the reliable connectivity needed to maintain the communication.  
The proposed algorithm is constructed in both the time and frequency domain in order to increase the quality of service (QoS) for mobile network users. The edge users experience enhanced throughput and reduced amounts of delay. The main issue for edge users is the worse channel condition because of the obstacles within the UEs and eNodeB. The scheduler is capable of analyzing the data about the delay and size of the packet of the users to facilitate the allocation of the resource blocks. Whenever a user is in communication with several eNodeB, its data could be transmitted using the superior quality channel for establishing capability aiming to increase channel quality. The scheduler separates UEs in spite of the level of the traffic. There are two groups: primary group and additional group. If the traffic is very high then the primary group is activated; otherwise the additional group is activated. According to the type of incoming packet, the quality of service maintains the priority-related scheduling technique. The optimization has been performed using the efficient scheduling algorithm where the highest priority component has the urgent queue for providing optimization. OFDM has been involved for the downlink communication procedure for providing 3 GPP LTE. The multi-carrier transmission and frequency division multiplexing (FDM) are the main parameters for better bandwidth allocation. The scheduler is indeed an important component for assigning and sharing the physical resources between different kinds of users.
## 6 Discussion
### Benefits
#### 6.1.7 Better Resource Sharing  
In a complex network like a knowledge-defined network, network resources should be efficiently and securely shared among legitimate network users. In order to achieve that task autonomously, smart contracts and blockchain can be utilized. For instance, in order to share edge network resources, a smart contract can be created containing service level agreements to share resources among multiple parties while securing access control policies [242]. Furthermore, resources can be traded among multiple network operators, leveraging the blockchain system and self-executing contracts to facilitate better resource trading and sharing in a multi-operator network environment [372]. Furthermore, the cost of resource usage can be efficiently paid using incentives, as blockchains offer incentive mechanisms.
## 4 Calibration Algorithm for Pixel and Steel Tape Display Error  
### Pixel Calibration  
For image recognition, the pixel resolution is directly related to the measurement accuracy, so pixel calibration is the first step of system calibration, as shown in Figure 7. In this design, a high-precision glass line ruler is used to calibrate pixels, and the measurement results are saved into the system. The specific calibration steps are as follows:  
1. Selection of the left scribed line and automatic recognition of the position of the cutting line (subpixel calculation), denoted as Pos_L;
2. Selection of the right scribed line and automatic recognition of the position of the line, recorded as Pos_R;
3. Calculate the spacing of the two lines: \(L=\text{Pos\_R}-\text{Pos\_L}\);
4. The high-precision glass line ruler gives the actual measured distance \(D\) of the two-notch line or edge and the pixel resolution is \(F_{\text{pix}}=D/L\).
# Optimizing the Honeycomb Spoke Structure of a Non-Pneumatic Wheel to Reduce Rolling Resistance
###### Abstract  
Traditional pneumatic tyres are prone to puncture or blowout and other safety hazards. Non-pneumatic tyres use a high-strength, high-toughness support structure to replace the \"airbag body\" structure of pneumatic tyres, which is made of fibre skeleton materials and rubber laminated layers, thus effectively avoiding the problems of blowout and air leakage. However, discontinuous spokes undergo repeated bending deformation when carrying loads, which leads to energy loss, of which the rolling resistance of non-pneumatic tyres is one of the main sources of energy loss. This paper focuses on the study of gradient honeycomb non-pneumatic tyres. Firstly, a finite element model was established, and the accuracy of the model was verified by numerical simulation and stiffness tests. Secondly, the order of the effect of different spoke thicknesses on rolling resistance was obtained through orthogonal test analysis of four-layer honeycomb spoke thicknesses. Then, four optimized design variables were selected in combination with the spoke angles, and the effects of the design variables on rolling resistance were analyzed in detail by means of the Latin hypercube experimental design. Finally, the response surface model was established, and the non-linear optimization model was solved by the EVOL optimization algorithm considering the tyre stiffness limitations so that the rolling resistance was minimized. The results of the study laid down theoretical and methodological guidance for the design concept and technological innovation of low rolling resistance comfort non-pneumatic tyres.  
non-pneumatic tyre; honeycomb spoke; roll resistance; DOE; structure optimization