esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results
### Stiffness Degradation  
Figure 5 presents the stiffness degradation curve of the specimen. The figure revealed a consistent trend in the stiffness degradation curve for each specimen, characterized by the highest stiffness in the elastic section. As the vertical displacement exceeded approximately 3 mm, the stiffness experienced a rapid degradation until reaching a vertical displacement of approximately 5 mm. However, due to the constraint effect of the circular-end aluminum tube, the stiffness degradation was mitigated and the curve transitioned into a section of gentle degradation.  
Figure 5: Stiffness degradation curve.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Specimen No.** & \(K\)**/\(k\)N\(\cdot\)mm\({}^{-1}\)** & \(P_{y}\)**/\(k\)N** & \(P_{u}\)**/\(k\)N** & \(P_{s}\)**/\(k\)N** & \(SI\) & \(\mu\) & \(P_{FE}\)/\(k\)N & \(P_{u}\)/\(P_{FE}\) \\ \hline CA100-0 & 125.2 & 223.7 & 238.4 & 228.0 & 1.05 & 1.95 & 249.7 & 0.95 \\ CA115-0 & 90.4 & 280.1 & 295.4 & 345.3 & 0.86 & 1.88 & 305.0 & 0.97 \\ CA120-0 & 30.7 & 101.6 & 103.2 & 183.0 & 0.56 & 1.15 & 100.3 & 1.03 \\ CA130-0 & 153.4 & 352.3 & 365.6 & 323.7 & 1.13 & 1.79 & 367.7 & 0.99 \\ \hline \end{tabular} Note: \(K\) is the initial stiffness under axial compression, taken as the slope of the elastic section of the load-displacement curve. \(P_{y}\) and \(P_{u}\) are the yield load and ultimate load, respectively. \(P_{s}\) is the total theoretical ultimate bearing capacity of concrete and aluminum tubes. \(SI\) is the bearing capacity coefficient. \(M\) is the ductility coefficient, calculated using the equal energy method. \(P_{FE}\) is the ultimate bearing capacity calculated by finite element simulation.  
\end{table}
Table 3: Test result.  
The initial stiffness of the specimen gradually decreased as the _a/b_ increased. In comparison with specimens with an _a/b_ = 2.0, the initial stiffness of specimens with _a/b_ = 2.5 and _a/b_ = 4.0 decreased by 28.2% and 75.5%, respectively. This reduction could be attributed to the increased _a/b_, leading to a significant difference in the sectional moment of inertia between the strong and weak axes. Consequently, the test specimen experienced a high slenderness ratio around the weak axis, resulting in premature overall instability.  
For specimens with the same _a/b_, the stiffness degradation curves exhibited parallel behavior. However, as the aluminum content increased, the initial stiffness decreased, with the CA100-0 demonstrating an 18.4% lower initial stiffness compared with the CA130-0. This disparity could be attributed to the increased confinement coefficient of concrete due to the higher aluminum content. However, the lower elastic modulus of the aluminum alloy affected the synergistic performance with concrete, subsequently influencing the combined stiffness of the cross-section.
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 2 Experimental Procedure
### Dynamic Low-Velocity Pendulum Test  
A large-deflection dynamic bending test was performed with an unnotched cantilever set-up per ASTM D1822-21 standards [20]. An instrumented pendulum-type RESIL impactor with a 1.5 mm hemispherical head and 0.46 kg mass was used, and data were processed with a DAS 8000 system as shown in Figure 3. Rectangular specimens made from GSP, GSV, and GSH with dimensions 80 mm \(\times\) 14 mm were clamped 27 mm from the base, with the impact zone 22 mm above the fixture (as shown in Figure 3b). Single and multiple impact tests were conducted before and after seawater exposure to assess moisture effects. Single impacts used a 150\({}^{\circ}\) hammer release angle, 2.9 m/s velocity, and 1.93 l energy, while multiple impacts used a 60\({}^{\circ}\) release angle and 0.52 l energy. At least 5 samples were tested for each configuration in the single and multiple impact configurations to ensure statistical repeatability. Iterative adjustments ensured consistent damage levels across samples.  
Figure 3: Low-velocity impact bending test set-up: (**a**) RESIL impactor and (**b**) schematic of sample fixture description.  
Figure 2: Schematic of AE pencil break test set-up (sample size: 170 mm \(\times\) 30 mm) [11].
## 2 System Model
### Problem Formulation  
In this paper, we are interested in the problem of UiL contert delivery. Upon a user request, a decision should be made to recommend a list of \(N\) contents to the user. Such a decision should jointly consider the user interest and the current channel conditions of the user. The decision could be made at the BSs in a distributed fashion, or at the central server in a centralized fashion. In both cases, the users' interest profile and channel conditions should be periodically monitored.  
The performance of the UiL content delivery system can be evaluated by two metrics, both directly related to the user QoE. The first metric is sum user interests, which indicates the user satisfaction at the semantic level, while the second metric is transmission delay, which indicates the user satisfaction during the communication process. We assume that multiple users have different service classes and different delay requirements. The delay requirement of user \(u\) is denoted as \(\tau_{u}\). The recommendation algorithm needs to ensure that the contents requested by a user are delivered in time with high probability. Our problem formation aims to maximize the total user interests, under the condition that the probability for the transmission delay to be smaller than the given delay requirement is greater than a predefined parameter \(\xi\). In other words, the probability of satisfying each user's delay requirement should be greater than \(\xi\). Mathematically, the delay requirement can be written as  
\[\forall u,t,P(\frac{q_{u}^{t}}{R_{u}^{t}}=\frac{q_{u}^{t}}{\sum_{k=1}^{K}c_{uk }^{t}\rho_{uk}^{t}\times B}\leq\tau_{u})>\xi, \tag{11}\]  
where \(q_{u}^{t}\) represents the amount of traffic generated by user \(u\) at time slot \(t\), and \(R_{u}^{t}\) is the user's data rate at time slot \(t\). The problem of UiL content delivery can be formally stated as \[\begin{split}\text{max}&\quad\sum_{u=1}^{U}\sum_{f=1}^{F}r_{ uif}x_{uf},\\ \text{s.t.}&\quad\forall u,t,P(\frac{q_{u}^{t}}{R_{u}^ {t}}=\frac{q_{u}^{t}}{\sum_{k=1}^{K}c_{uk}^{t}\rho_{uk}^{t}\times B}\leq\tau_{ u})>\xi,\\ &\quad\forall t,B\times\sum_{u=1}^{U}\sum_{k=1}^{K}\frac{f(c_{uk} ^{t})}{\alpha_{uk}^{t}}\times\rho_{uk}^{t}\leq P_{T},\\ &\quad\sum_{f=1}^{F}x_{uf}=N,\\ &\quad\sum_{u=1}^{U}\rho_{uk}^{t}\leq 1,\\ &\quad x_{uf}\in\{0,1\},\rho_{uk}^{t}\in\{0,1\}.\end{split} \tag{12}\]  
The goal of our problem is to optimize the total interest over multiple decision variables \(x_{uf}\), \(\rho_{uk}^{t}\) and \(c_{uk}^{t}\). This implies that the tasks of content recommendation and content delivery (i.e., subcarrier and power allocation) are jointly executed. Here, constraints C1 to C4 correspond to transmission delay constraint, total BS power limit, number of recommended files, and orthogonal subcarrier allocation, respectively.
## 5 Conclusions  
In this paper, we have analyzed the performance of the SM-MIMO-FD DF relay system and derived the closed-form expression of the system SEP. Understanding the importance of the optimal power allocation to the SM-MIMO-FD relay system, especially in the case of imperfect SIC, we have proposed an optimal power allocation algorithm for the FD relay to minimize the system SEP. Both numerical and simulation results showed that the RSI has a substantial impact on the SEP performance. However, the SEP of the system with optimal power allocation is significantly lower compared with that of the system without optimal power allocation. This result confirms the effectiveness of using optimal power allocation for compensating the impact of the RSI in the SM-MIMO-FD DF relay system.  
The main contributions of L.V.N., B.C.N. were to create the main ideas and execute performance evaluation by extensive simulation while X.N.T., L.T.D. worked as the advisors to discuss, create, and advise the main ideas and performance evaluations together.  
This research received no external funding.  
The authors declare no conflict of interest.
# Flow Characteristics and Loss Models of the Transonic Radial Turbine
## 4 Loss Models
### Incidence Loss  
The incidence loss here refers to any work in the turning of the working fluid from its direction of approach to the rotor to the direction required by the blade passage. For an ideal case, the fluid flows into the rotor passage with the optimum incidence angle, which minimizes the entropy generation. However, the fluid may have deviations with the blade angle in most cases. Thus, the incidence loss is the contribution to the entropy generated when the turbine is operating away from its design point, and the rotor inlet flow angle is not equal to the optimum. Although various incidence loss models have been proposed previously, [PERSON] showed similarity of the results for various incidence loss models relative to the variation of the incidence angle. Equation (1) proposed by [PERSON] and [PERSON] [9] is a typical formulation of this mechanism, where \(W_{4}\) is the relative velocity at the inlet of the rotor, \(\beta_{4}\) is the incidence angle, and \(\beta_{4,opt}\) is the optimum incidence angle.  
\[L_{i}=\frac{1}{2}W_{4}^{2}\text{sin}^{2}\left(\beta_{4}-\beta_{4,opt}\right) \tag{1}\]
## 3 Experimental Program
### Setups for Quasi-Static Tension and Single-Yarn Pullout Tests  
The specimens were first glued to steel adapters and subsequently clamped rigidly in the machine with the help of steel rods; see Figure 5a. The quasi-static uniaxial tension tests were performed in an Instron 8501 hydraulic testing machine under a controlled displacement rate of 0.05 mm/s. The deformations of the gauge portion were measured by two linear variable differential transducers (LVDTs) connected to the adapters on both sides of the specimens. Additionally, the deformations, formation of cracks, and fracture processes were monitored optically, and subsequently evaluated using digital image correlation (DIC). Images with a resolution of 3456 \(\times\) 5184 pixels were taken with a Canon E05 700D camera at intervals of 5 seconds. The DIC evaluation was performed using the Aramis software by GOM GmbH.  
Figure 4: Specim production for single-yarn pullout tests: (**a**) cutting configuration; (**b**) final specimen dimensions and fixities.  
Figure 5: Test setups for: (**a**) quasi-static tension tests and (**b**) quasi-static single-yarn pullout tests.  
The quasi-static single-yarm pullout tests were performed in a Zwick Roell 1445 testing machine at a displacement rate of 0.05 mm/s. The specimens were glued at their ends inside two aluminum rings, which were fixed in the machine, as shown in Figure 5b. LVDTs were fixed at both sides of the rings to capture the slip of yarns.
# Considerations about Parameters Estimation into a Minimum Variance Control System
## 3 Case Studies for Validation
### Case 3. Electrical Load/Unload  
The last study considers the process disturbed by a successively load/unload (resistive load \(\pm\)5%), generated by connecting/disconnecting an electrical consumer at generator terminals.  
Case 3a: For the beginning, the closed-loop is considered in the same tuning conditions of the control system as in the previous cases: \(\rho=0.01\), \(\lambda=0.995\), \(\sigma^{2}=0.01\). Figure 7a,b describe the terminal voltage (controlled output), respectively the excitation voltage (controller output) and show very good performances (small overshoots, small settling times, good control penalization). In Figure 7c is presented the time variation of the estimated parameters and, based on them, in Figure 7d is calculated and presented the estimated gain (in closed-loop) of the controlled process (by \(P_{0}\) and \(P_{1}\)' being noted the successive operating points). In addition, in Figure 7e there are presented a zoom of the result from Figure 7d, noticing that the process gains (in successively operating points \(P_{0}\) and \(P_{1}\)') are \(k_{o}=0.8612\) and \(k_{1}\)' = 0.8653.  
Figure 7: _Cont._Case 3b: Considering the open-loop process, for the same disturbances (successive load/unload) and excitation voltage change as in the previous case, the Figure 8a shows the real estimates of the linearized process model and the Figure 8b shows the calculated (estimated) gain of the process.  
A simple comparison of the results from Figures 7c and 8a shows (as in previous cases) a different time evolution of the parameter estimates in closed-loop, respectively, open-loop. However, based on these different estimates, in steady-state regimes, the computed process gain in open-loop (Figure 8b) and in closed-loop (Figure 7e) are similar in value. This last case once again confirms what has been already stated in the previous case studies.  
Therefore, all performed tests lead to the same conclusion: in steady-state, although the closed-loop estimates do not converge to the real estimates calculated in open-loop, in both regimes (open or closed-loop) the corresponding gains (calculated based on them) are similar in value. Thus, the simulation tests validate the analytical demonstration presented in the paper.
## 3 Transport System Modelling and Optimization
Traffic flow is a representation of people and goods moving through points and linear elements of a transport network [57, 58]. The flow appearing on the origin nodes, i.e., at the entrance to a transport system, is usually identified with the market demand for transport services, while in the case of system engineering, it means the environmental impact on the studied system. However, the flow entering the transport system through the destination nodes shows the impact of the system on its surroundings. The manner in which the traffic flow passes through a transport network is called a traffic organization. This phenomenon is subject to the following requirements imposed on traffic flow in the course of executing transport system processes:  
* condition of satisfying transport demand,
* condition of non-negativity of traffic flow,
* condition of traffic flow additivity,
* condition of maintained traffic flow. Figure 1 shows the algorithm for creating a mathematical model of a transport system (TSMA) focused on optimizing the distribution of traffic flow. Defining a transport system is the initial modelling stage. It is followed by the construction of a transport network graph that indicates the origins and destinations of the traffic flow. The next step involves determining  
a set of links \(L\) that join individual transport nodes \(w_{ij}\). A set of movement pairs of O-D \(E\) and all possible travel routes \(P^{ab}\) for each pair of O-D (\(a\), \(b\)) are determined for such a defined structure. Depending on the adopted criterion (set of criteria), each important element of the transport network must have an appropriate characteristic assigned to it. The final stage is to determine the traffic flow volumes for a selected set \(E\) of pairs O-D.  
Figure 1: Transport system model creation algorithm (TSMA). The structure of a model created as a result of the algorithm implemented provides grounds for an analysis of the transport system and enables optimization of the distribution of traffic flow in the transport network according to an assumed criterion (set of criteria).
## 1 Introduction
Several research questions were highlighted as follows:  
1. What is the suitable control reference parameter for stance assistance using PICAFO? Controlling the walking gait at the stance phase is not limited to the joint stiffness only. Other mechanical properties that can serve as the control reference parameter is also presented, such as motion parameter and assistive torque [11]. These mechanical properties are possible to be controlled using active actuators such as tracking and generation of the ankle motion path using electric motors [22; 23; 24; 25], and assistive torque generation using pneumatics muscle [26; 27; 28] for balancing the body [29]. However, in this study, PICAFO was equipped only with MR brake as the actuator, which is not suitable for implementation of sophisticated control reference such as motion path. On the other hand, additional actuators are not a wise choice due to consideration of complex structure and overall weight of the PICAFO [30]. Because of that, mechanical properties such as ankle torque and ankle angular velocity were chosen to be investigated in this study since these can be controlled using an MR brake [31]. 2. What critical parameter is suitable for estimation of the control reference parameter? Estimating the desired control reference requires information such as ground terrains, walking styles, user's anthropometrics, and other factors. In the previous study on AFO with MR brake, the control reference estimation which associated with the walking style such as the walking speed has been reported [32; 33]. However, without investigation on the effect of the user's anthropometric such as body mass index (BMI) to the control reference. BMI is commonly used to describe human identity [34]. Thus, an additional critical parameter such as the BMI is expected to increase the control reference estimation accuracy. To sum up, this preliminary study investigates the control reference parameters for stance assistance using PICAFO. The chosen control reference parameters were ankle torque and ankle angular velocity. On the other hand, the chosen critical parameters were walking speed and BMI. Section 2 further discussed the research materials and methods. Meanwhile, Sections 3 and 4 discussed the research findings and analysis, respectively.
# Combination of Variable Loads in Structural Design
## 1 Introduction
### Notation, Limitations  
The notation outlined in the Eurocodes [1; 2] is applicable. This article addresses the combination factor calculation as they appear in the design equations of common variable loads--including snow, wind, and imposed load--in fundamental design scenarios typical of standard construction practices focusing on the combination of the ultimate limit state (ULS) loads and solely on basic linear elastic cases excluding considerations of nonlinearity, plasticity, and fatigue [5].  
Later in this article, the notation \(\psi\) is used for the combination factor. It corresponds to Eurocodes' notation \(\psi_{0}\). The Eurocodes include other combination factors in the SLS: \(\psi_{1}=\) frequent factor and \(\psi_{2}=\) quasi-permanent factor caused by load intermittency. Loads are not typified here as frequent or quasi-permanent. Therefore, combination factors \(\psi_{1}\) and \(\psi_{2}\) are not needed in the codes in this load model when normal loads are combined.  
The notation \(\psi\) in this article applies mainly to the combination factor of the codes and sometimes the value calculated for load proportion of 0.5. When confusion may arise, explicit notations are used: \(\psi_{0.5}\) and \(\psi_{\rm code}\).  
The terms \"reliability\" and \"safety\" are used here in relation to the application of a safety factor. For example, when a factor increases safety by 1%, it means reducing the safety factor corresponding to the target reliability by 1%.
## 3 Network Slicing in 5G Networks
### Energy Demand of NSs Provided as Services of Mobile Virtual Network Operators
This enables the MVNO to obtain a competitive market advantage by introducing applications and services not offered by competitors or the MNO. However, when compared to the light MVNO, the realization of medium MVNO incurs a significantly larger share of the OPEX and CAPEX. The medium MVNO has a nonnegligible impact on the control and management of NS energy consumption, since it possesses more network resources compared to light MVNO, while the control and management of the energy consumption of other network elements leased from the MNO are in the charge of the MNO. Thus, the NS realized as the medium MVNO is also an energy consumer, which energy consumption is smaller than the NS part of the network energy consumption of the MNO. A **full MVNO** is realized as NS with a comprehensive standalone core network and switching elements that utilize the radio access network of other MNO(s) to connect user devices to its network (Figure 5). The full MVNO is a type of E2E NS where elements of the network, spanning from the core through the transport and up to the radio access part of the network, are sliced by the MNO that is to be leased by the MVNO. The main advantage of full MVNO is that it can incorporate its own network and application innovations into the leased NS, offering end-users services that the MNO does not possess. Moreover, this slice, with additional network and application advantages or innovations, can be offered again to another MVNO as a service. Additionally, the advantage of using a full MVNO as an NS is that it has all the functionalities of an MNO, while the drawback is its significantly high OPEX and its share in CAPEX. Compared to previously described MVNO types, another drawback is the necessity for extensive knowledge regarding the functioning and management of telecommunication systems of the full MVNO, which entails hiring skilled personnel. The stated significant share in the CAPEX of the full MVNO is also reflected in the full MVNO being a high energy consumer. Due to its ownership of more network resources than previously described MVNOs, the MVNO's overall energy consumption is significantly larger compared to previously described MVNOs. However, the possibility of full MVNO to influence the energy consumption policy and management of network resources is also large.