esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory of Surrounding Rock Support Characteristics and Timing for Secondary Lining Support
### Timing for Secondary Lining Support  
The existing theories regarding the timing for the installation of secondary lining support primarily include the ultimate displacement criterion, the deformation rate criterion, and the minimum support resistance criterion. The key components of each criterion are outlined below:  
1. Ultimate Displacement Criterion According to the ultimate displacement criterion, the optimal timing for the installation of a secondary lining occurs when the accumulated displacement reaches 87% [32], 88% to 93% [33] of the predicted final deformation. This criterion, which accounts for the complete release of surrounding rock stress and deformation, is considered more reliable and reasonable than the deformation rate criterion.
2. Deformation Rate Criterion The deformation rate criterion suggests that the surrounding rock should be allowed to deform sufficiently to utilize its self-bearing capacity. Under this criterion, the secondary lining should be installed once the deformation rate of the surrounding rock falls below a critical value. This critical deformation rate is determined based on on-site monitoring data, with the final deformation predicted accordingly. The Chinese Specification JTGT 3660--2020 [34] suggests that the peripheral displacement rate should be less than 0.1 mm/d to 0.2 mm/d, or that the crown settlement rate should be below 0.075 mm/d to 0.15 mm/d.
3. Minimum Support Resistance Criterion For the minimum support resistance criterion, the timing for the installation of the secondary lining is determined through a regression analysis of on-site monitoring data related to support resistance. This approach identifies the moment when the support resistance experienced by the secondary lining structure reaches its minimum. While this criterion is theoretically straightforward, it requires substantial on-site monitoring efforts. This criterion can be determined during the design phase by integrating the GRC and SCC.
## 3 Results and Discussion
### Pull out Test  
The effect of the different amounts of PVA fibers on the bonding behavior of the deformed steel bar and the geopolymer matrixes is shown in Figure 12A. It can be seen that the peak pull out load of the deformed steel bar is increased with the increasing amount of PVA fibers An increase in displacement, at peak pull out load, was also noted with increasing concentrations of PVA fibers. After the first peak, more peaks were noticed in the post-peak region for the PVA fiber-reinforced specimens. This was because of the enhanced frictional bond of the deformed steel bar with the geopolymer matrix, which is attributed to the decrease in pores [49]. The improvement in the bond between the geopolymer matrix and the steel deformed bars was due to the refining of the microstructure into a homogeneous, dense, and compacted microstructure by the synergic effect of the PVA fibers and nanosilica. Nanosilica increased the amount of reactive silica in the geopolymeric system, resulting the formation of silicon-rich gel, which has compacted and improved mechanical properties. The alteration of large pores in geopolymeric structures, comprising the nanosilica and PVA fibers, was also observed, and it could be the reason behind the enhanced bond strength between the deformed steel bar and the matrix in the specimens [25]. The bond stress-slip curves of all the geopolymer pull out specimens are presented in Figure 12B. The bond strength increased by 6.55%, 13.78%, and 80.35% in the GP3, GP5, and GP7 specimens, respectively, with respect to the GP0 specimen. It has been reported that the increase in bond strength is not because of good chemical adhesion but is due to superior tensile strength of the specimens [50]. In another study [25], it was observed that geopolymer mortar specimens, with an addition of PVA fibers, depicted an increase in their tensile strength. Therefore, it can be concluded that the greater bond strength of the geopolymer specimens is due to their higher tensile strength. All the specimens were failed when the induced forces between the surrounding material and the steel deformed bar exceeded the maximum tensile strength of the geopolymer composite. It was observed that specimens with a higher amount of PVA fibers took more time before the splitting of the composite than the specimens with a lower amount of PVA fibers. During the test, GP0 experienced a larger crack than that of the GP5 specimen, as shown in Figure 13.  
Figure 12: Bond interaction between the matrix and the deformed steel bar explains (**A**) the pullout load—displacement curves; (**B**) the stress-slip curves.
# Outage Probability Analysis in Relaying Cooperative Systems with NOMA Considering Power Splitting
## 3 Performance Analysis
### Approximate Outage Performance  
Due to the involvement of an integral in the above expression for \(OP\) which is not closed-form in Proposition 1, the asymptotic \(OP\) expressions at high SNR regime in Proposition 2 are going to be expressed as the following tight upper bound function.  
**Proposition 2**.: _At high SNR regime, we can express the approximate expressions for \(OP\) with a and b standing for the communication via R and the direct transmission as_  
\[OP_{\left(\beta\rightarrow\infty\right)}^{\left(x_{1}\right)}\approx v_{1}-v _{1}\left(1-v_{2}\right)e^{\frac{1}{2}\mu_{2}}W_{-1,\frac{1}{2}}\left(\mu_{2} \right), \tag{21}\]  
_and_  
\[OP_{\left(\beta\rightarrow\infty\right)}^{\left(x_{2}\right)}\approx 1-\left(1-v _{1}\right)e^{\frac{1}{2}\mu_{2}}W_{-1,\frac{1}{2}}\left(\mu_{2}\right), \tag{22}\]  
_where \(\mu_{2}=\frac{d_{SR}^{m}d_{D}^{m}d_{SD}^{-m}\Omega_{\bar{\gamma}_{0}}}{\eta \rho\Omega_{\bar{\gamma}_{1}}\Omega_{\bar{\gamma}_{2}}}\)._  
Proof.: Similar to steps conducted in the proof for Proposition 1, the modified Bessel function of the second kind as, \(xK_{1}\left(x\right)\to 1\) can be upper bounded when \(x\to 0\). Therefore, at high SNR, the CDF in (17) with \(\beta\rightarrow\infty\) can be expressed again as  
\[F_{\gamma_{D,2,x_{1}}}\left(\gamma_{0}\right) =1-\frac{1}{\Omega_{\bar{\gamma}_{3}}}\int_{0}^{\infty}\left(e^{ -\frac{x}{\Omega_{\bar{\gamma}_{3}}}}\mu_{1}K_{1}\left(\mu_{1}\right)\right)dx\] \[\leq 1-\frac{2}{\Omega_{\bar{\gamma}_{3}}}\sqrt{\frac{d_{SR}^{m} d_{SD}^{-m}\Omega_{\bar{\gamma}_{0}}}{\eta\rho\Omega_{\bar{\gamma}_{1}}\Omega_{ \bar{\gamma}_{2}}}}\int_{x=0}^{\infty}e^{-\frac{x}{\Omega_{\bar{\gamma}_{3}}}} \sqrt{x}K_{1}\left(2\sqrt{\frac{d_{SR}^{m}d_{RD}^{m}d_{SD}^{-m}\gamma_{0}}{ \eta\rho\Omega_{\bar{\gamma}_{1}}\Omega_{\bar{\gamma}_{2}}}}\sqrt{x}\right)dx\,\,. \tag{23}\]  
Thanks to the integral identity in ([28], Equation (6.643.2)), we have  
\[\int_{0}^{\infty}x^{\mu-\frac{1}{2}}e^{-ax}K_{2v}\left(2\beta\sqrt{x}\right)dx =\frac{\Gamma\left(\mu+v+\frac{1}{2}\right)\Gamma\left(\mu-v+\frac{1}{2} \right)}{2\beta}e^{\frac{\beta^{2}}{2\alpha}}\alpha^{-\mu}W_{-\mu,v}\left( \frac{\beta^{2}}{\alpha}\right), \tag{24}\]  
Following that, we derive  
\[F_{\gamma_{D,2,2,2}^{\left(\alpha_{1}\right)}}\left(\gamma_{0}\right)\leq 1-e^{ \frac{1}{2}\frac{d_{SR}^{m}d_{SD}^{m}d_{SD}^{-m}\Omega_{\bar{\gamma}_{3}}\gamma_ {0}}{\eta\rho\Omega_{\bar{\gamma}_{1}}\Omega_{\bar{\gamma}_{2}}}}W_{-1,\frac{1} {2}}\left(\frac{d_{SR}^{m}d_{RD}^{m}d_{SD}^{-m}\Omega_{\bar{\gamma}_{3}}\gamma_ {0}}{\eta\rho\Omega_{\bar{\gamma}_{1}}\Omega_{\bar{\gamma}_{2}}}\right). \tag{25}\]  
To this point, \(e^{-x}=1-x\) can be approximated when \(x\to 0\) as in ([28], Equation (1.211.1)) from (12). After some algebraic manipulations, (21) and (22) are obtained for the proof of Proposition 2.
## 5 Security Analysis of Strike
### Cryptanalysis Attacks
#### 5.3.4 Differential Attack  
Differential attacks try to reveal or reduce the time needed to reveal the secret key by performing intensive and comprehensive analysis over pairs of plaintext and ciphertext. In our Strike cipher, the keystream generator produces a completely new _SKey_ at each iteration, where each lightning strike has its own unique set of attributes. Thus, attributes are scrambled and randomised at the end of each iteration, ensuring the uniqueness of each generated _SKey_. Accordingly, two identical plaintexts will be converted into two different ciphertexts. Hence, our Strike cipher is resistant to differential attacks.
## 1 Introduction
Reliability always has to be established for newly developed measurement tools [26; 27; 28; 29] as well as when applying a commonly used tool according to a newly established procedure [30] and when there is no information in the literature about the reliability of a widely utilized tool according to a well-known methodology [31]. As [PERSON] et al. (2023) emphasized, enhancing the consistency and accuracy of measurements is critical in ensuring that clinical outcomes are reliable and meaningful [32]. This statement was echoed by [PERSON] et al. (2023), who highlighted the need forbetter evaluation standards in clinical research to ensure that findings are reproducible and applicable in everyday practice [33]. Reliability refers to the extent to which a measurement can be replicated [34; 35], meaning it should consistently yield the same results under similar conditions. There are three types of reliability: intra-rater, inter-rater, and test-retest. Intra-rater reliability is the variation in obtained data when one examiner records measurements across at least two trials [36]. In turn, intra-rater reliability (within-observer consistency) concerns whether there is agreement between assessments of the same participants by the same rater on a minimum of two different occasions [37]. On the other hand, to assess inter-rater reliability, a sample of \(n\) participants should be examined using the same tool and according to the same methodology with a minimum of two examiners [38]. Distinct types of inter-rater reliability can be distinguished; however, in terms of our study, the most proper is the type of reliability that involves selecting a random sample of \(k\) examiners from a larger group, where each examiner evaluates every participant, meaning that each examiner rates a total of \(n\) participants. From a practical point of view, inter-rater reliability seems more valuable than intra-rater reliability. However, in determining whether or how much the measure is user-dependent, comparing intra-rater and inter-rater reliability is crucial [39]. Therefore, this study aimed to develop an sEMG-based index for quantifying knee muscle coactivation purposes in healthy male recreational athletes during the YBT-LQ and determine its reliability.
## 3 Methodology
### Target-Level Sentiment Classification Using Traditional Methods
For fine tuning, the model was trained on each fold for 10 epochs using default parameters ('model': <class 'pyabsa.tasks.AspectPolarityClassification.models._lcf_fast_tsa_t_v2.FAST_LSA_T_V2>, 'optimizer': 'adamw', 'learning_rate': 2e-05, 'cache_dataset': True, 'warmup_step': -1, 'deep_ensemble': False, 'use_bert_spec': True,'max_seq_len': 80, 'patience': 99999, 'SRD': 3, 'dlcf_a': 2, 'dca_p': 1, 'dca_layer': 3, 'use_syntax_based_SRD': False,'sigma': 0.3, 'lcf': 'cdw', 'lsa': False, 'window': 'lr', 'eta': 1, 'eta_lr': 0.1, 'dropout': 0.5, 'l2 reg': 1e-06, 'num_epoch': 10, 'batch_size': 16, 'initializer': 'xavier_uniform_,'seed': 52, 'output_dim': 3, 'log_step': 5, 'dynamic_truncate': True,'srd_alignment': True, 'evaluate_begin': 0,'similarity_threshold': 1, 'cross_validate_fold': -1, 'use_amp': False, 'overwrite_cache': False, 'pretrained_bert': 'yangheng/deberta-v3-base-absa-v1.1'). The entire 5-fold cross-validation process was completed in 8 min on a standard NVIDIA RTX 4090 GPU. We remark that VADER and DistilFinRoBERTa provide sentence/document level sentiment analysis, while DeBERTa-v3-base-ABSA-v1.1 is developed to perform target/aspect-based sentiment analysis.
# Aerodynamics and Motion Performance of the H-Type Floating Vertical Axis Wind Turbine
## 3 The Dynamical Equations of the Floating VAWT  
### The Nonlinear Coupled Motion Equations  
[PERSON] et al. [16] established the nonlinear coupled motion equations of the Spar platform, and verified the equations by comparing with test modal. In this paper, the method is used to analyze the dynamic motion of the floating VAWT. Regarding the whole floating VAWT as a rigid body, considering the inertia force (moment), damping force (moment), restoring force (moment), mooring force, wind and wave loads, the surge-heave-pitch coupled equations of the floating VAWT can be presented as follows,  
\[(\mathbf{M}+\mathbf{A}_{\infty})\ddot{X}(t)+\int_{0}^{t}\mathbf{h}(t-\tau)\dot{X}(t)d\tau+ K(X(t))X(t)=F(t,X,\dot{X}), \tag{25}\]  
where \(M\) is the mass matrix of the floating VAWT, \(A_{\infty}\) is the mass matrix at infinite frequency, and \(h(t-\tau)\) is the retardation function accounting for the fluid memory effect \(X\), \(\dot{X}\) and \(\ddot{X}\) represents the displacement, velocity and acceleration vectors of the floating VAWT at its center of gravity, respectively, and \(X=[\xi_{1}\,\xi_{3}\,\xi_{5}]^{T}\). \(K\) is the restoring matrix, including the hydrostatic restoring, restoring of the mooring system, and the nonlinear hydrostatic restoring. \(F(t,X,\dot{X})\) is the exciting force vector, which includes the Froud-Krylov force \(F_{fk}(t)\), diffraction force \(F_{d}(t)\), wind loads \(F_{w}(X,\dot{X},t)\) (including the aerodynamic loads \(F_{w}{}^{a}(X,\dot{X},t)\) and the wind loads \(F_{w}{}^{p}\) calculated by the wind pressure theory), and hydrodynamic viscous force \(F_{v1}~{}=~{}D_{1}\dot{X}+D_{2}\dot{X}\Big{|}\dot{X}\Big{|}\), where \(D_{1}\) is the matrix of viscous damping coefficient,  
\[F(t,X,\dot{X})~{}=~{}F_{fk}(t)+F_{d}(t)+F_{w}(X,\dot{X},t)+F_{v1}, \tag{26}\]  
Figure 4: Validity checking of the aerodynamic loads.  
For the Spar type floating foundation, the nonlinear coupling between the heave and pitch motions is significant. Referring to the nonlinear heave-pitch coupling motion equations of the deepsea truss Spar platform [16; 17], the restoring matrix can be written as follows,  
\[K=\left[\begin{array}{ccc}f_{1}(\xi_{1})&0&0\\ 0&f_{3}(\xi_{3})+\rho gA_{w}&-\frac{H_{d}}{2}\rho gA_{w}\xi_{5}\\ 0&\frac{1}{2}\rho g(\
abla+2A_{w}\overline{GM})\xi_{3}&\rho\
abla g\overline{ GM}\end{array}\right], \tag{27}\]  
where \(f_{i}\left(\xi_{i}\right)\) (\(i=1,3\)) represents restoring stiffness of the mooring system in surge and heave of the floating VAWT. _Av_ is water plane area of the floating foundation, \(\overline{GM}\) is the initial stability height of the floating VAWT, \(\
abla\) is the displacement, \(\rho\) is the density of seawater, and \(g\) is gravity acceleration. The hydrostatic restoring loads are calculated on the static equilibrium position of the floating VAWT.
## 4 Results and Discussion
### Scenario 3: Passive Signage  
The impact of passive signage on evacuation performance was analyzed using the configuration presented in Table 5. The simulation focused on a single case where the pre-evacuation delay remained constant, but the delay at intersections was varied. This study evaluated average evacuation times for miners with uniform stamina and those with varying stamina levels.  
According to the simulation results depicted in Figure 11, there was a gradual increase in the average evacuation time as the delay at intersections was increased for both groups of miners. In general, the longer the delay at intersections, the greater the increase in the average evacuation time. Additionally, the results shown in this figure are similar to those in Figure 9. However, in Figure 9, intersection delays are constant, while in Figure 11, pre-evacuation delays are held constant.
# Simulation Analysis of a Three-Degree-of-Freedom Low-Frequency Resonant Mixer
## 3 Dynamic Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer
### Harmonic Response Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer
#### 3.2.3 Amplitude-Frequency Response of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer  
Through computer simulation analysis, the measurement surface is selected on the payload assembly, vibration-isolating assembly and excitation assembly, respectively, and the directional deformation of the three components is obtained in the software. The direction is set as Z-axis, and the displacement in the Z-axis direction is obtained. The harmonic response analysis results were exported, and the amplitude-frequency response curves are shown in Figure 6.  
As shown in Figure 6, when the equipment operates at a frequency between 57 Hz and 62 Hz, the amplitude of each assembly experiences a sudden change, indicating that the equipment is working near the third-order natural frequency and resonance is occurring. Calculations reveal that at the third-order natural frequency, the amplitude of the payload assembly is approximately 356.45% of that of the excitation assembly, achieving amplitude amplification. At this point, the amplitude of the payload assembly is 4.42 mm, which can facilitate efficient mixing of the material at the payload assembly location; the amplitude of the excitation assembly at the third-order natural frequency is 1.24 mm, a smaller amplitude that is beneficial for the stable operation of the drive assembly connected to the excitation assembly.
## 4 Experiments and Results
### Test Performance of Different Algorithms  
In order to test the performance of MTVs generated by three algorithms in the actual boundary scan test, we actually inject these faults into the module under test by connecting the corresponding jumper cables and using the MTVs generated by these three algorithms to do the boundary scan test on it. We use the method of weighted sampling to extract the shorted-circuit fault to be injected from Figure 4. Each time, we will extract one or two faults to simulate the 2-order and the 3-order short-circuit fault. The weight of a 2-order or a 3-order short-circuit fault is the ratio of the probability of it and the sum of all the probabilities of all the 2-order and 3-order shorted faults. After injecting the faults extracted, we connect the JTAG of the module to our boundary scan test system EasyScan-USB IEEE 1149.1 and use the system to inject the MTVs. For each algorithm, we do the 200 experiments. The rates of misjudgment or confusion in the test using MTVs generated by each algorithm are shown in Table 5. The results show that the MTVs generated by our method are less frequently misjudged or confused than those generated by walk-1 and PSO. It shows, from a posterior perspective, that our MTVs have a lower failure rate when tested.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 3 Architecture of the Network Emulation Platform
### Platform Characteristics Analysis  
Through the above designs, the proposed emulation platform effectively provides a realistic, flexible, and extensible experimental environment for SN.  
Virtual emulation nodes have the same functionality as physical hardware and can execute exactly the same code in a real deployment. Meanwhile, the proposed emulation platform supports the joint emulation method among virtual nodes, physical nodes, real networks and real links, bringing credible emulation results.  
Integrating with SDN and traffic control mechanism, the networking of platform can change automatically like that of a real space communication environment (especially, time-varying link quality and dynamic link connection). In addition, various network protocol architectures are reconstructed by container images. These realize the reliable and flexible emulation of different SN scenarios  
The platform architecture has extendibility including the horizontal extension of hardware resources and the access of external physical emulation devices. The main controller offers a novel unified control for the connection relationships between emulation nodes under different switches through flow tables. These features allow multiple nodes with huge individual differences to become a part of the proposed platform and to be managed.
## 4 Analysis
### Security Analysis  
We now analyze the security of our proposed scheme against several attacks, including: (i) the local forged checksum is defined as the checksum generated by compromised nodes; (ii) the relay nodes' false reports on the time difference are defined as the false time difference reported by compromised relay nodes; (iii) the relay nodes that deferred forwarding are defined as the reports delayed by the compromised relay nodes deliberately; and (iv) the collusion of relay nodes is defined as the attack that is launched by several compromised nodes collaboratively.
# HBIM: Background, Current Trends, and Future Prospects
###### Abstract
During the data acquisition phase, various techniques are employed to gather geometric information (e.g., laser scanning, photogrammetry, and total station measurements) [8] along with non-geometric data (e.g., historical context, architectural details, material properties, functional and structural pathologies, and condition assessments). The documentation process plays a crucial role in collecting all relevant non-geometric information necessary for the development of the model. Next, the collected data must be processed, verified for accuracy, and evaluated for their applicability to the HBIM model's purpose [9]. The geometric data are then used to create a 3D model of the building using appropriate BIM software, while non-geometric data are either embedded directly into this geometric model or stored in an external database linked to it [7]. This 3D information model is subsequently used for the management of the building, serving multiple purposes with the overarching aim of guiding the conservation plan and planning any necessary interventions [8; 10]. Additionally, any new data obtained during the management phase, whether through model analysis or further tests on the building, should be processed and incorporated into the HBIM model to ensure they remain up-to-date. This workflow is illustrated in Figure 1.  
Figure 1: HBIM general workflow. Over the past decade, research on HBIM has focused on three fundamental aspects: data acquisition processes and modeling techniques, the use of HBIM for heritage building management, and the conduction of structural condition analyses within HBIM environments. These areas aim to address the main challenges and applications of HBIM methodology. While early research primarily concentrated on data capture techniques and model creation, attention in recent years has shifted toward the use of HBIM models for building management and condition analysis. The introduction of real-time monitoring systems and the development of digital twins of heritage structures within an HBIM environment has great potential for transforming building management by optimizing lifecycle efficiency, resource use, and sustainability [4]. This paper offers a comprehensive review of historic building information modeling, tracing its origins and development and exploring current standards, protocols, and data collection methods. It delves into modeling methodologies, common data requirements, and the growing range of HBIM applications. Additionally, the paper discusses the potential for integrating structural analysis tools within HBIM environments, highlighting their expanding role in heritage preventive preservation and management. This review aims to analyze the major advancements in the field of HBIM over recent years and how these developments have improved the management of architectural heritage. Furthermore, it seeks to examine the main current research areas in HBIM to identify and discuss key strengths, weaknesses, challenges, and potential future advancements.