esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Cross-Section Model Based on Polygonal Discretization
### Cross-Section Stiffness Matrix  
In the FEM formulation of the proposed model, the stiffness matrices \(\mathbf{k}_{\mathrm{s}}\) of the control cross-sections are used for the derivation of the element-stiffness matrix \(\mathbf{K}_{E}\). For each control cross-section with coordinate \(x\) along the element length, its stiffness matrix \(\mathbf{k}_{\mathrm{s}}\) is formulated as the contributions of all of the n Voronoi regions of area \(A_{cp,i}\) of the control points \(cp,i\) with coordinates \((y_{cp,i},z_{cp,j})\) (Figures 11f and 12). [PERSON] et al. [5] used the same formulation for their fiber model, where the areas \(A_{cp,j}\) were those of the fibers. The modulus of elasticity \(E_{cp,i}\) of each control point is computed from Equations (8), (16), (22) and (25), depending on the loading-unloading-reloading branch of the concrete constitutive law in which its stress-strain state belongs. Following these definitions, the relation of the cross-sectional stiffness matrix \(\mathbf{k}_{\mathrm{s}}\) is as follows:  
\[\mathbf{k}_{S}=\left[\begin{array}{c}\sum\limits_{cp,i=1}^{n}E_{cp,i}\cdot A _{cp,j}\cdot y_{cp,i}^{2}\\ \hd  
* Equilibrium is always satisfied throughout an RC member after selecting linear interpolation functions assuming no intermediate loads on the member. Compatibility of deformations is achieved by computing the member's end deformations by integrating those of the distributed control cross-sections.
* The inelastic response of the RC members can be calculated without particular computational difficulties, even in cases of lightly reinforced members or members under high axial load. Special convergence criteria and methods for avoiding numerical problems have been introduced in the main FE algorithm (Flowchart FC1), which are critical for the proposed model. Numerical problems can arise for a number of reasons, the main ones being the following:
* The use of the Newton-Raphson method, whose convergence becomes unstable when the tangent stiffness tends to zero.
* The advanced degree of inelasticity of the structure. This can happen when a number of control cross-sections of elements have yielded or failed.
* The partial failure of cross-sections, caused either by concrete cracking or by yielding of the reinforcement bars.
* The method of calculating the cross-section's internal forces (more details in Section 6.2)
* Imposing large values of load increments.
* Accumulative error, regarding the unbalanced forces, from previous analysis steps. The main and critical points of the FEM algorithm are described in the next sections, in reference to flowchart FC1 of Appendix A.
## 3 Architecture Proposed  
This section describes the architecture proposed to manage a RINA network for IoT environments by using an SDN controller. Figure 3 presents the layered RINA and SDN integration architecture proposed in this work.  
The proposed architecture follows the SDN generic architecture that is based on three layers: the application layer, control layer and infrastructure layer, to facilitate the adoption of RINA in the current SDN networks. The architecture presents three key components in  
Figure 3: RINA and SDN integration architecture proposed.  
the control layer and the infrastructure layer to enable the management of RINA devices (RINA-based IoT devices, RINA-based IoT gateways, RINA-based EdgeNodes, and the RINA-based core). These components are the RINA Management Agent (MA), the RINA manager application, and the RINA SBI.  
The RINA MA enables the control of RINA devices. There are two kinds of MAs: CDAP-based and the Hypertext Transfer Protocol (HTTP)-based. The CDAP-based MA follows the RINA principles of design and implementation detailed in [4]. Meanwhile, the HTTP-based MA is a particular solution for enabling the progressive adoption of RINA in cloud-based scenarios. Specifically, the HTTP-based MA facilitates the deployment of L2 VPN to enable seamless communication between edge nodes and cloud application by using RINA capacities. Section 3.3 presents the L2 VPN functionalities and explains the HTTP-based MA functionalities.  
The RINA manager application facilitates the control of RINA devices under the same management DAF by using CDAP messages. A DAF is the collection of two or more cooperating application processes that exchange information using DIF's IPC services. In this case, the RINA manager and the CDAP-based MA make up the management DAF. Section 3.1 explains the management DAF's main characteristics and functionalities.  
The RINA SBI driver abstracts the RINA manager and HTTP-based MA functionalities to enable a seamless integration with current controller applications. Section 3.2 details the RINA SBI driver design and implementation.
# Load-Carrying Capacity of Bailey Bridge in Civil Applications
## 5 Results and Discussion
### Deck Members
#### 5.2.2 Cross Beams  
The load-carrying capacity of cross beams presented in Table 7 relates to the configuration of three cross beams per panel, with the mutual distances of 1400 + 1290 + 318 mm. The typical 10-inch I-section (currently RSJ 254/114/37) with 102 mm openings in the web was verified. The values are specified for both single and double-story systems with both double and triple-truss arrangement. The configuration of the timber deck system has only a small influence on the load-carrying capacity of cross beam. Additionally, the load-carrying capacity of cross beams is only partially dependent on the span of the main girders. It is due to the fact that the cross beams are additionally loaded by stresses resulting from their function as part of the transverse half-frames ensuring the stability of the main girders. However, the differences among the BB models are small, and are mostly influenced by the rotational stiffness of the connection to the main girders. The differences, although small, are therefore caused by the presence of two or three transom clamps, depending on whether the analysed BB has double and triple-truss arrangement.  
According to Table 7, it can be stated that if three original cross beams per panel are used, they sufficiently satisfy the requirements for exclusive load-carrying capacity introduced. Moreover, the cross beams are also able to meet requirements for acceptable normal load-carrying capacity, i.e., the conventional lorries with weight up to 28 tons can cross the bridge without any traffic restrictions.  
Table 8 presents the reduction in load-carrying capacity of cross beams if only two cross beams per panel of BB are used with the mutual distances of 1608 and 1440 mm. This seems to be not very reasonable solution. The exclusive load-carrying capacity is around the required limit, but the value of normal load-carrying capacity has decreased, especially in case of double-truss BB is this reduction more severe.
# Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
## 3 Results and Discussion
### Interfacial Bond Strength Analysis
#### 3.2.1 Influence of Interface Form  
Table 5 indicates that, for the direct shear specimens with a concrete strength grade of C35, the interfacial shear bond strength from high to low was in the order of triangular interface, trapezoidal interface, and smooth interface. The bond strengths of the specimens with triangular and trapezoidal interfaces were much larger than that of the specimen with a smooth interface (i.e., 178.76% and 156.64% larger than that of the smooth interface, respectively). This result was obtained because the bond strength of the specimen with a smooth interface was provided only by friction force and the chemical adhesive force generated with the existing concrete when CGM was poured. Meanwhile, the bond strengths of the specimens with trapezoidal and triangular interfaces were mainly provided by the shear strength of serrated concrete and CGM itself at the interface, and the friction force and chemical adhesive force between them were relatively small. The bond strength of the specimen with a triangular interface was 8.62% higher than that of the specimen with a trapezoidal interface because the actual bonding area between CGM and concrete of the triangular-interface specimen was larger than that of the trapezoidal-interface specimen.
## 3 Mirror Optimization Design
### Optimization Results  
Using the optimization method described in Section 2, the Pareto optimal solutions were obtained and are shown in Figure 4, where the circles represent optimization results obtained during iterations.  
According to the obtained Pareto optimal solution set, the preferred optimal solution could be selected based on the weight of two objectives. In this case, the average surface shape error \(\overline{\text{RMS}}\) in the three directions was required to be less than 1 nm, and the total mass of the mirror was required to be as small as possible. Therefore, \(\overline{\text{RMS}}=0.956\) nm and \(\text{Mass}=717.9\) g was chosen in the optimal solution set as the final optimization solution. At this point, the surface shape error was be obtained in three directions, respectively, as shown in Figures 5-7, and the optimization parameters as well as the specific optimization results are shown in Table 2. From the results in Table 1, we can see that although the average surface shape error was less than 1 nm, there were two components that were more than 1 nm, which were \(\text{RMS}_{\text{y}}\) and \(\text{RMS}_{\text{z}}\). The surface shape error in the Y direction (\(\text{RMS}_{\text{y}}\)) was the biggest of those in the three directions due to the effects of gravity. After removing the rigid-body motion, the aberrations in X direction were much smaller than those in the other two directions.  
\begin{table}
\begin{tabular}{c c} \hline
**Components** & **Value** \\ \hline \(D_{1}\) & 64.4 mm \\ \hline \(D_{2}\) & 46 mm \\ \hline \(L_{1}\) & 13 mm \\ \hline \(L_{2}\) & 21.6 mm \\ \hline \(\text{RMS}_{\text{x}}\) & \(5.68\times 10^{-7}\) mm \\ \hline \(\text{RMS}_{\text{y}}\) & \(1.24\times 10^{-6}\) mm \\ \hline \(\text{RMS}_{\text{z}}\) & \(1.06\times 10^{-6}\) mm \\ \hline \(\overline{\text{RMS}}\) & \(9.56\times 10^{-7}\) mm \\ \hline Mirror Mass & 717.9 g \\ \hline \end{tabular}
\end{table}
Table 2: Optimization results of the mirror.  
Figure 4: Pareto optimal solutions.  
Figure 5: Surface shape error in X direction.  
Figure 6: Surface shape error in Y direction.
# Real-Time Maintenance Optimization with Industrial Internet of Things
## 3 Models of Conventional and Real-Time Optimization
### IIoT-Supported Framework for Real-Time Maintenance Strategy Optimization  
A general IIoT architecture suitable for collecting real-time state information from a production system and supplying relevant data to a real-time optimization software consists of several integrated layers (see Figure 1).  
The system begins with an edge layer, where sensors and control units are installed on production equipment to continuously monitor parameters such as temperature, pressure, vibration, runtime, and fault signals. These devices are connected to an edge gateway that performs basic local preprocessing, including noise filtering, data aggregation, sampling, and real-time event detection (e.g., exceeding critical thresholds or detecting abnormal behavior).  
The processed data are then transmitted through a real-time communication protocol such as MQTT, AMQP, or WebSocket to a centralized processing system. Data are structured into well-defined channels or topics, enabling organized, hierarchical data flow.  
This is followed by a middleware layer responsible for integrating and processing the incoming data streams. A real-time data engine at this layer can detect significant events, maintain dynamic state models for each machine (such as condition index or reliability score), and store recent data in a high-performance, real-time or time-series database.  
The optimization layer accesses this information through an open API, typically RESTful or WebSocket-based. Real-time optimization algorithms can query the system to retrieve the latest state of equipment, reliability indicators, maintenance history, or operational constraints. These algorithms can also send back decisions or recommendations, such as rescheduling tasks or initiating maintenance actions.  
Optionally, a cloud-based component may be integrated to handle long-term data storage. This supports machine learning applications, historical analysis, and KPI tracking, helping the system to evolve over time and enabling advanced analytics for process improvement. Overall, this IIoT architecture ensures continuous feedback between the physical production environment and intelligent optimization tools, enhancing decision-making and operational efficiency.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing
### RAN Slicing
#### 3.2.3 RRM Procedures  
The gNB includes a set of RRM procedures that govern the allocation of NR cell resources across existing slices, in such a way that shortage of resources in one slice does not break the SLA of another slice. There are two fundamental RRM procedures: admission control and scheduling.  
The task of admission control is to admit or reject the establishment requests for new radio bearers. Admission control can be based on number of users (RRC connections) or number of DRBs. The first option allows limiting the number of UEs accessing a specific slice based on SLA requirements. The second option is based on reserving enough DRBs for each slice, according to their estimated data volume.  
The scheduling allows the gNB to dispatch available Physical Radio Blocks (PRBs), i.e., frequency-time resource grids, across the different slices, in such a way that the QoS requirements associated with their PDU sessions can be fulfilled. These requirements are expressed with the 3 GPP 5G Quality Indicator (5 QI) [10].  
At a very high load, admission control provides the scheduler with sufficient resources to secure QoS of all the admitted users. To that end, it is very important to design an efficient admission control algorithm that can take into account the overall resource situation, the priorities of users based on their category level, the QoS of the in-progress request and the QoS requirements of the new radio bearer requests.  
The operation in the admission control and scheduling procedures is, in both cases, based on the configuration of the following per slice quotas: dedicated minimum slice quota (optional), minimum slice quota (mandatory) and maximum slice quota (mandatory). Figure 6 provides a summary of these three quotas. These quotas need to be specified for each RRM procedure, since the managed NR cell resources are different:  
* For admission control, the NR cell resources correspond to either RRC connections (option 1) or DRBs (option 2). For option 1, it is the CU-CP which configures the quotas. For option 2, it is the CU-UP.
* When scheduling, the NR cell resources correspond to PRBs, based on which per slice quotas are defined.  
In this work, we will focus on scheduling aspects. Table 2 shows different examples on how to configure the slice quotas for scheduling. As seen, depending on the values set for these quotas, the slice can be profiled into different categories.
# Touch, Texture and Haptic Feedback: A Review on How We Feel the World around Us
## 4 Haptic Feedback
### Cutaneous Feedback
#### 4.2.4 Electrotactile Feedback  
The use of electric stimulation in discriminating different surface textures has also seen increased interest, specifically in the field of prosthetics. Most electrotactile stimulation involves the use of microelectrodes which are implanted beneath the skin and directly onto the targeted nerve to provide direct stimulation through electric voltage which mimicshuman electrical signals based on the type of sensation being simulated [134; 135; 136]. These methods provide their users the ability to distinguish shape, stiffness, and texture recognition to a high degree and show promising results; however, they come with significant drawbacks such as being limited to invasive surgery and overall system stability and reliability in extended usage [137], which can severely hinder their uses outside of controlled environments. One promising way of electric stimulation, however, can potentially solve the previous issues and instead makes use of electrodes placed on the skin to deliver electric voltage to the skin to stimulate the nerve endings without the use of invasive surgery [138] and this process is known as transcutaneous electrical nerve stimulation or (TENS) and is used primarily for providing sensory information in a prosthesis, one of which involves tactile feedback [139].  
Currently, the use of TENS as a means of tactile stimulation is limited primarily to prosthetic devices as is generally the most common way amputees can experience sensory information. [PERSON] et al. used a 2 \(\times\) 8 electrode array attached to the upper arm which was used to evoke finger sensation by transforming the fingertip forces to current amplitude, allowing for the distinguishing of shapes and sizes [140]. [PERSON] et al. used TENS to stimulate force sensations in the hands and fingertips allowing patients to output force levels that would not be possible otherwise [141]. Other studies have also shown the use of electroductile stimulation for improved grasping and proprioception [142; 143; 144].  
While research regarding TENS is limited mostly in the field of prosthetics, there is also a possibility of its use in the field of traditional haptics. [PERSON] et al. used a combination of electroductile and vibrotactile feedback to improve hand movements [145] in both amputees and non-amputees. [PERSON] et al. used a microphone to distinguish sound caused by the friction of rubbing different texture types and transforming them into electrical stimulation [146]. The non-invasive approach of TENS systems may allow them to be integrated into other haptic feedback systems such as kinesthetic feedback systems which usually lack any form of cutaneous feedback; however, the high electrical voltage required is a significant issue.
## 3 Results and Discussion
### Bread Texture  
The results of the analysis of bread texture and the crumb moisture content are presented in Table 3. The hardness of the crumb is the most studied parameter in bread texture studies, which is described as the maximum force recorded during the first crumb compression [29]. Enrichment of wheat bread with PL had no negative influence on crumb hardness. The highest crumb hardness value was observed for CB (8.2 N), and the lowest was observed for PB4 (6.2 N). When the bread volume decreases due to gluten weakness from different additives, the crumb becomes denser and harder [50]. However, this relationship was not found in the present study. The lack of this relationship can be attributable to the fact that PL increased the water absorption of flour. Consequently, a higher amount of water was added during dough preparation, and the crumb moisture was also higher (Table 1). The crumb with a higher moisture content was characterized by decreased hardness [51]. Moreover, the incorporation of PL into the bread recipe had no significant influence on other texture parameters, such as crumb springiness, cohesiveness,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **L*** & **a*** & **b*** & **TCD** \\ \hline CB * & 73.9 \(\pm\) 1.11 e ** & 2.83 \(\pm\) 0.62 d & 12.83 \(\pm\) 0.48 a & - \\ PB1 & 66.1 \(\pm\) 1.03 d & \(-\)0.37 \(\pm\) 0.27 a & 16.87 \(\pm\) 0.32 b & 9.3 \\ PB2 & 61.2 \(\pm\) 0.34 c & \(-\)1.10 \(\pm\) 0.19 b & 17.27 \(\pm\) 1.02 b & 14.0 \\ PB3 & 56.2 \(\pm\) 0.43 b & \(-\)1.67 \(\pm\) 0.52 c & 19.37 \(\pm\) 1.23 c & 19.4 \\ PB4 & 54.2 \(\pm\) 1.71 ab & \(-\)2.63 \(\pm\) 0.39 d & 17.90 \(\pm\) 0.98 c & 21.1 \\ PB5 & 53.8 \(\pm\) 2.30 a & 2.33 \(\pm\) 0.22 d & 18.13 \(\pm\) 1.07 c & 21.4 \\ \hline \hline \end{tabular} CB *–control bread, PB1, PB2, PB2, PB3, PB4 and PB5—read with 1, 2, 3, 4 and 5% of parsley powder, respectively. ** Values followed by the same letter (a–e) are not significantly different (\(p\) < 0.05), \(n\) = 3.  
\end{table}
Table 2: Color coordinates and total color difference (TCD) of the crumb.  
Figure 3: Control bread (CB) and bread with 1, 2, 3, 4, and 5% of parsley leaves (PB1, PB2, PB3, PB4, and PB5, respectively).  
and chewiness. These findings show that, in general, PL had little influence on crumb texture. Recently, [PERSON] et al. [52] studied the effects of the addition of sweet potato leaf powder on steam bread texture and found that crumb hardness increased linearly with the addition of potato leaves. On the other hand, such a relationship was not observed when powdered green leaves of leek were incorporated into wheat bread [53]. This demonstrates that the chemical composition of the additive used has a strong influence on bread texture.
## 1 Introduction
In this paper, we provide a review of published articles in the literature to comprehend the present state of research concerning software-defined networks-based vehicular networks with a particular focus on the articles whose contributions include modeling and implementation. Consequently, we performed a search on Google Scholar with the following keywords: software-defined networks, software-defined networks-based vehicular networks and modeling and implementation. In addition, we used the same keywords on other three research web engines, namely ScienceDirect, IEEE and ACM. Since SDN and VANETs are relatively new topics, we did not retrieve a huge number of papers that required an established protocol for evaluation and selection. Therefore, articles were manually selected or excluded if a given article provides clear modeling and implementation techniques. Other criteria were used in the selection such as significance, citation or rank of the publication venue. In this work, we mainly focus on providing implicit literature that focuses on classifying existing SDVN solutions based on their modeling and implementation. To the best of our knowledge, it is the first work that groups SDVNs based on their modeling and implementation schemes. Therefore, in this paper the main contributions are summarized as follows:  
* We first overview the current studies available in the literature on C-V2X technology in support of V2X applications. * We then present the different architectures and their underlying system model for LTE-V2X communications. * We also describe the keys ideas of software-defined networks and their concepts for V2X services. * We define four elements that are considered for modeling and implementations of SDN for vehicular networks. We then present a comparative analysis for existing schemes grouped according to their modeling and simulation concepts. * We provide a discussion and highlight vehicular adhoc network(VANET)' s challenges handled by SDN based vehicular network. The remainder of the paper is organized as follows: the current studies and technologies for V2X services are detailed in Section 2. A comparative study of architectures and a system model of LTE-V2X communication in the implementation of V2X services are discussed in Section 3. The modeling and implementation of software-defined vehicular networks for V2X is detailed in Section 4, together with a definition of SDN, before briefly discussing findings on the comparative study of existing SDN based vehicular network in Section 5. Finally we conclude our work in Section 6.