esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Literature Review of Reliable Provisioning for Virtual Network Function Chaining
## 2 Background
In [10], the authors present a Zero-Touch Proactive Failure Recovery (ZT-PFR) approach, which uses deep learning methods such as Soft-Actor-Critic (SAC) and Proximal-Policy-Optimization (PPO) to predict failures. The approach defines many VNF states, such as  
Figure 1: A service function chain. Figure 2: Provisioning of VNFs for a SFC. Normal, Warning, and Critical. The agent will be rewarded for correctly predicting the state change and for doing the necessary actions to provision backup VNFs. In [24], an algorithm is proposed to use Elastic Virtual Network Function Orchestration (EVNFO) to predict the workload and properly scale the network. A deep reinforcement learning method called Double Deep Q-Networks Placements (DDQP) is introduced in [25]. DDQP is used to deploy active and standby instances of backups in real-time. For larger networks, they used Deep Neural Networks (DNN). Despite the different approach, it still uses backup VNFs and paths for the recovery mechanism. The proposed solution has a higher cost for smaller networks compared to larger ones. In addition, [26] proposes using the Diversity Coding method to provide near-instant failure recovery for 5G networks. The method consists of creating a single redundant disjoint link to handle any single failure. The downside of the solution is that it cannot handle multiple failures. On the other hand, in distributed approaches such as the one introduced in [27], the Tabu search algorithm is used to handle the VNF placement to minimize cost and meet the performance requirements. The problem with the proposed approach is that it does not perform well in larger environments. In [28], authors the present the use of game theory to properly place VNFs in a distributed network. VNF Managers, which can deploy VNFs, act as players in a game. Each player can decide to activate itself to maximize its utility. Due to the distributed network, VNF managers autonomously adapt to the network without central control.
## 2 Bioactive Lipids in Inflammation and Bone Remodelling
### Endocannabinoids: Chemistry, Signalling and Pathophysiology  
Endocannabinoids (ECs) are endogenous lipids that engage the same cell receptors targeted by the main psychoactive ingredient of _Cannabis sativa_ and _Cannabis indica_, namely \(\Delta^{9}\)-tetrahydrocannabinol (THC) [74]. By binding to type-1 and type-2 (CB\({}_{1}\) and CB\({}_{2}\)) cannabinoid receptors (that belong to the GPCR family), and to transient receptor potential vanilloid 1 (TRPV1) channels, ECs are involved in several pathophysiological processes, acting both as neuromodulators [75; 76] and immunomododulators [77]. \(N\)-Arachidonovylethanolamine (AEA, also known as anandamide) and 2-arachidonovglycerol (2-AG) are the two best studied ECs, and with their biosynthetic--i.e., \(N\)-acylphosphatidylethanolamines-specific phospholipase D (NAPE-PLD) and diacylglycerol lipase (DAGL), for AEA and 2-AG respectively--and breakdown enzymes--i.e., fatty acid amide hydrolase (FAAH) and monoacylglycerolglycerol lipase (MAGL), for AEA and 2-AG respectively--they form the so-called EC system [77].  
Figure 2: Biosynthetic pathways of the main arachidonic acid (AA)-derived bioactive lipids. Targets that are up-regulated in microgravity are highlighted in green. COX: cyclooxygenase; CysLT: cysteinylleukin; DP: prostaglandin D receptor; EP: prostaglandin E receptor; FP: prostaglandin F receptor; IP: prostaglandin I receptor; LOX: lipoxygenase; LT: leukotriene; LX: lipoxin; PG: prostaglandin; PLA: phospholipase A; TP: thromboxane receptor.  
AEA biosynthesis starts with the \(N\)-acylation of membrane phosphatidyl-ethanolamine, catalysed by \(N\)-acyltransferase (NAT), to yield \(N\)-arachidonoyl-phosphatidylethanolamine (NArPE), which is then hydrolysed to AEA by NAPE-PLD [78]. Instead, degradation of AEA is catalysed by FAAH, which cleaves AEA into AA and ethanolamine [77].  
As for 2-AG, phospholipase (PL)C-catalysed hydrolysis of membrane phospholipids yields diacylglycerol, which is further hydrolysed by DAGL to produce 2-AG [77]; this is then cleaved into AA and glycerol by MAGL [79] and, to a lesser extent, by FAAH [80]. ECs can target several immune cells: AEA is able to suppress the pro-inflammatory activation and cytokine secretion in human lymphocytes [81] and dendritic cells [82], while 2-AG is able to enhance macrophage phagocytosis [83]. Their involvement in the control of inflammation is also suggested by several studies that point to EC-related dysfunctions as a cause of inflammatory pathologies, such as multiple sclerosis or inflammatory bowel diseases [49; 74; 84; 85; 86]. The main EC system elements are summarised in Figure 3.
# Active Control Loop of the BOROWIEC SLR Space Debris Tracking System
## 2 Satellite Laser Ranging Process  
The laser ranging measurements of an orbital object are carried out by using a pulsed laser source, as a measuring beam emitter with the specified beam divergence \(\theta\in(5^{\prime\prime},80^{\prime\prime})\), and a photon detector placed in the main focus of the receiving telescope operating mostly in the \(Az/El\) configuration (Figure 1). The detector is responsible for the acquisition of photons (\(E_{x}(t)\)) scattered by the orbital object (x) from the previously emitted laser beam. The laser range measurement is based on the emission of a laser pulse of known length (\(x*1\) ps, \(x*1\) ns) at the \(t_{START}\) moment of time, in the direction of the orbital object where some of the photons are scattered from the object's surface. The \(t_{STOP}\) moment of time, when the returning scattered photons are acquired by the photon detector, ends the measuring procedure. Therefore, the range measurement procedure consists of measuring the time interval \(\Delta t_{p}\), which is necessary for the light pulse to travel the station-object-station distance (\(\Delta t_{p}=t_{STOP}-t_{START}\)). The task of the telescope's movement control is to transform the ephemeris information \(\zeta_{x}\) about the position of the tracked object \(\kappa\) into the follow-up movement of the telescope's mount. Therefore, the position of the spatial point \(X_{\kappa}(t)\), \(Y_{\kappa}(t)\), \(Z_{\kappa}(t)\) should be transformed into the azimuth and elevation angles (\(Az(t),El(t)\)) for a given location of the SLR station. Due to the non-stationary nature of the process, the task is dynamic. Thus, the accuracy of the tracking process (mapping the motion of the telescope's mount) directly depends on the control process and the accuracy of the drive system and intermediate mechanics [15].  
Figure 1: Block diagram of the SLR tracking system.
## 2 Materials and Methods
### Determination of Antioxidant Activity  
The free radical-scavenging activity of \(D\). _reboudii_ was determined spectrophotometrically by three different methods. The DPPH\({}^{\bullet}\) assay was performed in accordance with a previously published protocol [14]. Briefly, 100 \(\upmu\)L of essential oil dissolved in methanol was added to 1900 \(\upmu\)L of a methanol DPPH solution. After vortexing, the solution was kept at room temperature for 30 min in the dark. The absorbance was measured at 517 nm, using a solution of DPPH in methanol as control. The scavenging activity on DPPH\({}^{\bullet}\) was expressed as inhibition percentage using the equation reported in [14].  
The ABTS\({}^{\bullet+}\) scavenging activity was tested according to [PERSON] et al. [15], and the results were compared with the absorbance values of the reference standards butylated hydroxytoluene (BHT) and \(\alpha\)-tocopherol. Briefly, before use, the aqueous 7 mM ABTS\({}^{\bullet+}\) solution was incubated for 15 h in the dark at room temperature. Then, 100 \(\upmu\)L of essential oil dissolved in methanol was added to 1900 \(\upmu\)L of the ABTS\({}^{\bullet+}\) solution. After vortexing, the mixture was kept at room temperature for 10 min in the dark, after which the absorbance was measured at 734 nm, using an aqueous solution of ABTS\({}^{\bullet+}\) as control. The scavenging activity on ABTS\({}^{\bullet+}\) was expressed as inhibition percentage using the equation reported in [15].  
The metal chelating activity by the ferrene-Fe\({}^{2+}\) complexation assay was carried on following the method of [PERSON] [16], using ethylenediaminetetracetic acid (EDTA) as reference standard. Briefly, 40 \(\upmu\)L of essential oil was added to 40 \(\upmu\)L of a 0.2 mM FeCl\({}_{2}\) solution. The reaction was initiated after the addition of 80 \(\upmu\)L of ferene solution (0.5 mM). The mixture was vortexed and kept at room temperature for 10 min. The absorbance was recorded at 593 nm, and the inhibition percentage of ferrous ion chelating was calculated using the equation reported in [16].  
Four different concentrations of \(D\). _reboudii_ essential oil were tested, namely 25, 50, 100 and 200 \(\upmu\)g/mL.
## 2 Materials and Methods
### Characterization  
The fibers were observed using an optical microscope (LV100, Nikon Corp., Tokyo, Japan) attached to an FL analyzer (LV-FLAN, Nikon Corp.). Diameters of the fibers were estimated using NIS Elements D, Nikon Corp. Fourier-transform infrared spectroscopy (FITC-IR) measurements of the fibers were conducted (Nicolet 380; Thermo Fisher Scientific Inc., Waltham, MA, USA). The spectra were obtained using the single reflection attenuation total-reflection (ATR) method with a Quest ATR accessory (GS-10800, Specac Ltd., Orpington, UK). The spectra were recorded with a resolution of 1.925 cm\({}^{-1}\) and baseline correction and ATR correction in the operating software were applied. The morphology and elemental composition of the fibers were evaluated using scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM and EDX, JSM-7001F; JEOL Ltd., Tokyo, Japan) with an acceleration voltage of 10 kV. In some cases, the specimens were coated with Pt-Pd using an ion-sputtering device (MC1000; Hitachi Ltd., Tokyo, Japan) to prevent charge-up.  
The mechanical properties of the fibers were evaluated to calculate the tensile strength using a universal tester (Autograph AGS-500 NJ; Shimadzu Corp., Kyoto, Japan). Initially, the diameter of the fiber required to calculate the tensile strength (MPa) that was obtained by dividing the test force (N) by the cross-sectional area of the sample was estimated by optical microscopic images. Afterward, the fibers were cut to 20 cm and placed in the apparatus while maintaining an initial gauge length of 10 cm. The stretching speed was set at 1 mm/min. The obtained stress-strain curves were analyzed using the Trapezium X software (Ver. 1.5.3, Shimadzu Corp., Kyoto, Japan). The test force (N) and the maximum tensile strengths (MPa) were expressed as the mean \(\pm\) S.D.  
The weight loss of the fibers was evaluated according to methods described in previous studies [28]. The fibers (1.0 m) were immersed in phosphate buffer saline (PBS, pH 7.4) and incubated at room temperature. The weight loss (%) was calculated using Equation (1).  
\[\text{Weight loss (\%)}=(W_{1}-W_{\text{F}})/W_{1}\times 100, \tag{1}\]  
where \(W_{1}\) is the initial weight of the fiber before immersion and \(W_{\text{F}}\) is the final weight of the fiber after the immersion experiments followed by drying in air.
# Urban Free-Space Optical Network Optimization
## 2 Problems Statement
### Network with Minimum Vertex Cost That Connects All Terminal Points  
Here, the previous problem is extended by adding weights to the vertices in the \(G_{rb}\) graph. This problem is motivated by the fact that the cost of placing the infrastructure may vary among different intermediate points; for instance, the building owners may demand different payments for using their roofs.  
**Definition 3**.: _Given a red-blue visibility graph \(G_{rb}=(R,B,E)\) and a vertex cost function \(w\left(v\right):R\rightarrow\mathbb{R}\), find a set of vertices \(R^{\prime}\in R\) of minimal overall cost that connects all the vertices in \(B\). The solution for this problem is a \(\hat{G}_{rb}\) with \(\arg\min_{v\in R^{\prime}}\sum w\left(v\right)\)._  
The previous unweighted variant can be reduced to the weighted variant herein by setting all vertex costs to \(1\). Thus, following the hardness of the unweighted variant [14], the weighted variant is also NP-hard (on general graphs).
## 3 Iterative Coupling Algorithm  
In what follows a detailed description of the submodelling \"two-way\" approach is given (see Figures 3 and 4). Three numerical models must be defined:  
Figure 4: Scheme of the coupled solution loop.  
Figure 3: Global/Local submodelling approach.  
Global Model--it is a global elastic model with a coarse mesh;  
Local Model--it is a submodel with a fine mesh in which elastic-plastic behavior is considered;  
Reference Model--it is a nonlinear model in which the discretization corresponds to that of the Local Model in the submodelled domain and to that of the Global Model in the remaining part of the domain.  
As shown in Figure 4, the steps to be followed in the coupled solution loop are:  
1. a global elastic analysis is performed on the Global model with a coarse mesh,
2. the interface displacements \(\mathbf{u}_{T}\) and nodal forces \(\mathbf{F}_{T}^{\delta}\) are collected (\(\Gamma\) is the interface curve),
3. a local nonlinear analysis is performed on the submodel applying \(\mathbf{u}_{T}\) as boundary conditions at the interface \(\Gamma\) (external boundary for the submodel),
4. nodal forces \(\mathbf{F}_{T}^{i}\), evaluated by the local analysis, are collected and subtracted from \(\mathbf{F}_{T}^{\delta}\) obtaining \(\Delta\mathbf{F}=\mathbf{F}_{T}^{\delta}-\mathbf{F}_{T}^{i}\),
5. if \(\|\Delta\mathbf{F}\|_{2}\) is greater than a prescribed limit \(\mathbf{\upxi}\); then the process comes back to step 1 where \(\Delta\mathbf{F}\) is applied to the Global model at the interface \(\Gamma\),
6. if \(\|\Delta\mathbf{F}\|_{2}\) is lower than a prescribed limit \(\mathbf{\upxi}\); the final solution is identified,  
where \(\|\Delta\mathbf{F}\|_{2}\) represents the Euclidean norm of the residual forces. Convergence can also be checked by evaluating the Euclidean norm of the interface displacements \(\|U\|_{2}\), evaluated by means of a numerical analysis of the Global Model.  
When non-conformal meshes are adopted for the Global and Local Models, specific algorithms are adopted to map displacements from the Global to the Local Model and forces in the inverse direction. The mapping algorithm adopted by ANSYS is a triangle-based linear interpolation [32].
# Some Key Aspects in the Mechanics of Stress Transfer Between SRG and Masony
## 3 Discussion of Results
### Load Responses
#### 3.2.1 Influence of SRG Matrix Width
The average peak load \(\overline{P^{*}}\) was greater for specimens with a matrix width equal to 70 mm (\(\overline{P^{*}}\) = 11.2 kN) and 90 mm (\(\overline{P^{*}}\) = 11.4 kN) mm when compared to 50 mm (\(\overline{P^{*}}\) = 7.6 kN), but there was no significant difference between the 70 mm and 90 mm specimens.
## 3 System Modeling
### Personalized Demand Queues  
In the InS system, in situ data is stored in servers with matching performance. Subsequently, different servers will generate data access demands continuously. However, there is a disparity in the update frequencies of the in situ data across different servers, which leads to differences in the volume of generated data access demands. To better ensure response speed, this study designs personalized demand queues to receive data access demands: each type of server's data access demands are recorded with the round count in the corresponding demand queue. Each demand queue \(Que_{i}\) can be represented as follows:  
\[Que_{i}=\{M_{i},R_{i},N_{i},L_{i},\psi_{i}\},\quad i\in\{low,medium,high\} \tag{7}\]  
where the queue \(Que_{i}\) is used to receive access requests for in situ data generated by server \(T_{i}\). To characterize the properties of the queue, this study introduces the following key variables: \(M_{i}\) denotes the maximum request capacity of the queue; \(R_{i}\) represents the current remaining available capacity, which is a positive integer constrained by the server's own performance. \(N_{i}\) records the number of access demands that have already been served in the queue, and \(L_{i}\) denotes the cumulative delay generated by all completed access demands in the queue; both are positive integers. \(\psi_{i}\) indicates the maximum number of access demands the queue can process in a single time step when it is selected, also a positive integer, reflecting the server's processing capability. \(|Que_{i}|\) denotes the current number of access demands in the queue.  
The access demands generated by different servers are recorded with the number of rounds in the corresponding queue \(Que_{i}\). The scheduling demand can be expressed as follows:  
\[\text{Dem}=\{\text{Data},C,T^{\text{in}},T^{\text{out}}\} \tag{8}\]  
where \(C\) represents the occupied demand queue capacity. \(T^{\text{in}}\) is the enqueue round of the access demand, initialized as the current round number \(T^{\text{now}}\). \(T^{\text{out}}\) is the dequeue round of the access demand, initialized as empty, and when it is responded to, it will be set to the current round number \(T^{\text{now}}\).
## 3 Security and Privacy-Preserving Solutions for IoE
### Blockchain-Based Security Solutions
#### 3.1.4 Blockchain-Based Access Control Solutions  
In IoE systems, access management is critical to prevent unauthorized devices from connecting to the network. Blockchain-based access control mechanisms can enhance security by integrating with Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) models [82; 83]. Blockchain-based access management:  
* _RBAC_: Determines access permissions based on user roles.
* _ABAC_: Dynamically determines access permissions based on user behavior.  
The integration of blockchain-based RBAC/ABAC systems with smart contracts is illustrated in Figure 5.  
Figure 5: Blockchain-based access control model.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Protocol** & \begin{tabular}{l} **Proof Size** \\ **(Bytes)** \\ \end{tabular} & \begin{tabular}{l} **Proving** \\ **Time (ms)** \\ \end{tabular} & \
\begin{tabular}{l} **Verification** \\ **Time (ms)** \\ \end{tabular} &
\begin{tabular}{l} **Trusted Setup** \\ **Resilient** \\ \end{tabular} & **IoE Suitability** \\ \hline zk-SNARK & 192 & 1299 & 1138 & Required & No & High: efficient, small size \\ zk-STARK & 6657 & 552 & 52 & Not required & Yes & Medium-High: quantum secure, large size \\ Bulletproofs & 737 & 6756 & 899 & Not required & No & Medium: suitable for lightweight systems \\ Ligero & \textasci{-}1024+ & Medium & Medium & Not required & Yes & Medium: balanced performance \\ \hline \hline \end{tabular}
\end{table}
Table 5: Key technical features of common ZKP protocols and their suitability for IoE applications.  
A user sends an access request to an IoE system. This request is evaluated by the RBAC/ABAC policy engine operating on the blockchain. Based on the policy evaluation, a smart contract decides whether to grant or deny access. If the access criteria are met, the system responds with access granted.
## 1 Introduction
The quasi-static methods mainly include the force-based method (FBM), displacement-based method (DBM), and detailed equivalent static analysis numerical method (DESANM) [7; 8; 9; 18; 19; 31; 32; 33; 34; 35; 36; 37; 38; 39]. Coupled and decoupled approaches and numerical dynamic analyses are too complex to be suitable for engineering design, and the quasi-static methods applicable to engineering design do not consider the effects of different soil properties  
In conclusion, the selection of seismic calculation methods and parameters does not consider the influences of the special geological conditions in various regions, so their usefulness is limited. The seismic analysis of subway station structures in loess areas is yet to be codified. In the existing research results, the seismic performance of underground subway station structures in loess is rarely researched. For the practical engineering of subway underground structures in a loess area, there is no reference seismic experiment, reliable quasi-static calculation method, or seismic parameter-calculation method yet available. Based on the above problems, herein, a modified DBM was proposed and its application was compared with the most commonly used methods of analysis (FDM, DBM, DESANM, and full dynamic-time history analysis (FDTHA)). The results were validated using data from a shaking table test conducted on the basis of a case study (the Feitian Road underground subway station in Xi'an). The modified DBM offers good accuracy, a simple model, rapid modeling, and easy convergence. It provides engineering designers with a seismic design and analysis method for underground structures with convenient application and high precision. Therefore, the results obtained in this study can be considered useful to designers who are required to address the seismic design of underground structures. In Section 2, the main seismic calculation methods of underground structures were analyzed, and the methods of calculation of foundation reaction spring stiffness parameters and formation horizontal relative displacement parameters were explored. In Section 3, the shaking-table test scheme design and the FDTHA simulation modeling of a subway station in a loess area were conducted. In Section 4, the results of shaking-table testing and the FDTHA numerical simulation were studied, and the seismic calculation methods for the main underground structures were compared. In Section 5, the DBM was modifiedaccording to the experimental results and simulation results, and the accuracy of the modified MDB was verified.
## 6 Discussion
### Dynamic Modelling
#### 6.2.1 Bolt Diameter  
As the load cell is dropped onto the concrete box, momentum is transferred from the load sphere to the bolt through the concrete encasing. As diameter of the bolt increases, there is no apparent increase of displacement. As the testing environment is a closed system, momentum and thus kinetic energy absorbed by the system should not change, which is reflected by the initial trend of the absorbed energy vs. displacement graph of the varying bolt diameters. However, as the bolt diameter influences the maximum shear displacement of the bolt, as the bolt diameter increases, the longer the bolt takes to reach failure, resulting in the bolt working longer and thus absorbing more energy. This is represented in the later stage of the absorbed energy vs. displacement graph, where bolts with larger diameters shows slightly more absorbed energy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline
**Bolt Diameter (mm)** & \(f_{y}\) = 500 MPa & \(f_{y}\) = 565 MPa & \(f_{y}\) = 633 MPa & \(f_{y}\) = 847 MPa & \(f_{y}\) = 922 MPa & \(f_{y}\) = 1382 MPa \\ \hline
18 & 36.2 & 36.9 & 45.1 & 41.6 & 43.0 & 49.5 \\
20 & 43.6 & 44.3 & 51.0 & 45.1 & 45.8 & 60.7 \\
23 & 45.8