esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The accumulative damage of a carbon fiber concrete specimen under fatigue load destroys the local conductive network near the microcrack, which leads to the increase of the whole volume resistivity. Resistance measurement has a great potential role for damage monitoring and the evaluation of carbon fiber reinforced concrete in the whole fatigue process. One study reported that electrical resistance change (ERC) may provide real-time monitoring and qualitatively estimate bending fatigue damage evolution of carbon fiber composites [17]. The degree of disorder of the resistance change increases with the increase of the stress level. The shape of the resistance change rate-fatigue cycle curve can reflect the stress level and the damage type of a specimen. By observing the change of direct current resistivity of carbon fiber polymer matrix composites, it was found that the resistance decreases irreversibly due to the damage of the matrix at the adjacent fiber junction, which increases the probability of adjacent fiber contact in the initial stage of fatigue loading [18]. As the fatigue increases, the resistance increases, but the degree of increase is diminished. [PERSON] et al. [19] carried out tensile fatigue tests on a 2024-T3 aluminum plate specimen and revealed the decrease in strength due to cumulative damage and the relationship between the resistance variation of the damaged specimen and the electromechanical impedance (EMI) response. The change of EMI and resistance were used to monitor the local electrical properties in order to evaluate the decrease of the material strength. Moreover, an evaluation model for fatigue damage, EMI response, and resistance change was established. In the above fatigue tests, whether under tensile or bending conditions, the volume conductivity of polymer or cement-based composites decreases irreversibly with the increase of fatigue times [15; 20]. Therefore, this phenomenon can be used to monitor the fatigue cumulative damage. As far as we know, there are few reports of fatigue damage between resistivity and cycle times at different temperatures and stress levels for conductive polymer-based intelligent reinforcement materials. In this paper, CNFPUC was taken as the research object; the variation of resistivity was measured in the bending condition at different temperatures by UTM-30 asphalt mixture multifunctional test equipment. Four-point bending fatigue life tests at different stress levels were carried out at corresponding temperatures to study the relationship between the fatigue cumulative damage and the resistivity change.
## 1 Introduction  
Metaverse technology is expected to bring many innovations to society, culture, and the economy by providing realistic services in various fields such as manufacturing, construction, education, healthcare, and social networking while suppressing time and space constraints [1; 2; 3; 4]. Especially in metaverse fields such as education, gaming, and healthcare, research on presence and immersion has been actively conducted to provide more realistic experiences, and such presence is an essential element of a metaverse environment [5; 6; 7; 8; 9; 10]. There is an ongoing discussion on how to integrate sensory information such as the visual and auditory information of users in realizing presence, and up to now, auditory factors have been used mainly as a supplement to visual information. However, recently, the realization of enhanced presence has become an important task, and the realization of a lifelike auditory environment is becoming important [11]. In general, questionnaire evaluation methods such as the presence questionnaire (PQ) and the Slater-Usoh-Steed presence questionnaire (SUS-PQ) are used to identify presence, and recently, auditory factors have been treated as a major factor to improve presence, and the need to evaluate them has emerged [12; 13].  
Existing questionnaires for evaluating presence focus on visual presence and cover various factors such as emotion, ease of use, and familiarity arising from the user's sensory interaction with the avatar and the system, but there is little reflection of auditory factors [14; 15; 16]. In particular, considering that the sense of presence is evaluated through factors such as the illusion created by the user's sensory information, the cumulative user experience (UX) of using the system, and the experience formed after using the system, it seems that it can be used if evaluation factors related to the sense of presence are added to the existingUX evaluation factors [10; 17; 18]; however, the existingUX evaluation contents have a limitation: it is difficult to evaluate the auditory part due to its general characteristics [19].  
Against this background, sinceUX evaluation factors affect the understanding of presence, and presence evaluation factors act to understandUX [17; 18], extracting common components of AUX evaluation factors and auditory presence evaluation factors may help to identify auditory experience evaluation factors considering presence enhancement in metaverse environments. Therefore, the study aims to develop an auditory experience evaluation questionnaire considering presence by conducting a literature review and primary and secondary expert interviews to improve the presence of the metaverse environment, and to derive the evaluation components considering auditory presence and AUX through questionnaires, PCA, and RA.
# Neighbor Discovery for IPv6 over BLE Mesh Networks
## 3 6 LoBLEmesh Neighbor Discovery  
In RFC 7668, the 6 LoWPAN ND was simplified to perform neighbor discovery for 6 LoBLE with a star topology. Based on that, this section proposes 6 LoBLEmesh ND for those nodes in the domain with BLE mesh topology. The neighbor discovery protocol based on BLE 5.0 is introduced, including link setup, address auto-configuration and address update. Different from the neighbor discovery protocol proposed in RFC 7668, the multi-hop communications and duplicate address detection mechanism are applied to the 6 LoBLEmesh ND in our approach.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 1 Introduction
The primary objectives of this work were as follows:  
* To experimentally analyze DoS attack scenarios targeting critical components in HLF networks. * To evaluate the impact of these attacks on performance metrics such as transaction latency, throughput, and node-level resource usage. * To propose practical countermeasures that improve the resilience of permissioned blockchains to such attacks. These objectives directly address the identified research gap by grounding the experiments in HLF's architecture and known configuration behaviors. We have selected the specific attack scenarios in our experiments based on their practical relevance to real-world HLF deployments, their exploitability via configurable components, and the relative lack of empirical analysis in prior work. Previous studies extensively explored DoS attacks in permissionless blockchains, such as mempool flooding and selfish mining in PoW systems [10; 11; 17], but comparatively few have examined attack surfaces in permissioned platforms like HLF. Among those that do, [PERSON] et al. [16] highlighted performance bottlenecks in HLF under a transaction load, while [PERSON] et al. [18] and [PERSON] et al. [5] discussed theoretical vulnerabilities in consensus mechanisms and peer-to-peer communication layers. Inspired by these studies, we focused on practical scenarios targeting components central to HLF's modular design: the ordering service (including Raft consensus), peer endorsement, MSP misconfiguration, and CouchDB as the state database. These components have either been identified as critical performance bottlenecks or as areas prone to misconfiguration in enterprise deployments [19; 20]. In contrast, we excluded attacks specific to anonymous or incentivized environments, such as Sybil-based or incentive manipulation attacks, because they exploit features not present in HLF's permissioned trust model. Our selection of attack scenarios reflects both a synthesis of identified vulnerabilities in the literature and a practical assessment of what can be realistically executed and measured in a controlled HLF environment. By aligning attack designs with system-specific configurations and literature findings, this study bridged the divide between descriptive security analysis and actionable resilience strategies. The remainder of this paper is structured as follows: Section 2 provides background on permissioned blockchains and the architecture of Hyperledger Fabric (HLF). Section 3 reviews related work on known attacks targeting HLF. Section 4 outlines the study's methodology, including the threat model. Section 5 presents the experimental setup and results. Section 6 discusses the findings in detail, analyzes the root causes of DoS vulnerabilities, and offers mitigation recommendations. Finally, Section 7 concludes the paper by summarizing key insights, acknowledging limitations, and outlining directions for future research.
## 3 Formulation of the Routing Problem  
Let \(N=\{N_{i}|1\leq i\leq L_{N}\}\) be the set of available nodes in a vehicular network with constant disruptions and non-fixed topology, and let \(A\in N\) be a given node in that set (Figure 1). Given the fact that there are no predefined paths and the connections are intermittent, the nodes in the network must act opportunistically, taking advantage of any node that gets into their communication range, because whenever these encounters happen, the opportunity of replicating a message arises. In those situations, \(A\) has to decide on a node to start a transfer, and several criteria can be used for this decision, but ultimately, \(A\) would like to choose the node with better capabilities of further spreading the messages until hopefully they get to their destination. Following this approach, the routing problem can then be expressed as finding the best next hop (BNH) for the messages. That is, from all \(k\) nodes that \(A\) is connected to in a given moment, the one, \(N_{x}\), with better fitness \(f_{x}\) must be determined, in terms of its current features \(x_{1},\ldots x_{n}\). Furthermore, in order to optimize the communication conditions, not only must the best next hop \(B\) be selected, but we can also detect the best next message (BNM) to be transferred. That is, based on its current attributes \(y_{1},y_{2},\ldots,\ y_{m}\), we must be able to select from the message queue \(M=\{M_{i}|1\leq i\leq L_{M}\}\) the message \(M_{y}\in M\) with the best fitness \(f_{y}\). Because neural networks have the power to learn very complex non-linear patterns, they are the perfect fit for what we are trying to achieve here, so we can model both optimization scenarios as binary classification tasks to allow us to quantify the capabilities of such nodes \(N_{i}\) as a function \(F\) of some of their characteristics \(x_{i}\) as \(f_{x}=F(x_{1},x_{2},\ldots x_{n})\) and the capabilities of such messages \(M_{i}\) as a function \(G\) of some of their characteristics \(y_{i}\) as \(f_{y}=G(y_{1},y_{2},\ldots y_{n})\).  
Figure 1: Opportunistic encounters for routing in vehicular delay tolerant networks (VDTN) and the message queue in a host.
## 4 Discussion
### RsmX-2 Is Part of Gac-Rsm System under Exclusive Control of GacA  
Despite the similarity between the _rsmX-1_ and _rsmX-2_ sequences, the expression of _rsmX-2_ differs from that of _rsmX-1_. The few mutations in the UAS (Figure 1B) of _rsmX-2_ may explain the 8-fold decrease in the expression level. Our data confirm that RsmX-2 sRNA is a member of the Rsm sRNAs family. Indeed, the exclusive overexpression of _rsmX-2_ in the _gacA_ mutant suppresses the effect of _gacA_ deletion, suggesting that the four Rsm sRNAs share a redundant function, which appears to be the sequestration of RsmA/E proteins.  
In the absence of GacA in _P. aeruginosa_ and _P. brassicacarum_, transcription of _rsmY_ and _rsmZ_ is still achieved but to a lesser degree, suggesting the involvement of additional regulatory pathways [7; 18]. We previously demonstrated the activation of _rsmZ_ expression, which depends on an enhancer sequence located in the _rpo5_ coding region. This suggested the role of additional transcriptional factors as part of the complex network controlling the expression of _rsmZ_. We showed that the conserved palindromic UAS required for GacA-controlled sRNA genes in Gammaproteobacteria is essential but not sufficient for the full expression of the _rsmZ_ gene in _P. brassicacarum_ NFM 421 [46].  
Finally, unlike the two novels recently discovered sRNAs, RsmV [47] and RsmW [48], in _P. aeruginosa_, which transcription is independent of the GacS-GacA, _rsmX-1_ and _rsmX-2_ expression is exclusively GacS-GacA-dependent.
## 2 Analysis of the Influence of Friction Coefficient on Synchronous Performance
### Evaluation Index of Synchronization Performance
#### 2.1.1 Shift Time  
The total time \(t\) used for shifting is composed of the time \(t_{1}\) taken for picking up the gear, the time \(t_{2}\) used for pre-synchronization, the time \(t_{s}\) used for synchronization, and the time \(t_{4}\) taken for the combination--of these, the proportion of synchronization time \(t_{s}\) is larger. The phase represented by the synchronization time has the greatest influence on the sliding-friction work and shock degree [20]. The following mainly studies the synchronization time \(t_{s}\).  
\[t=t_{1}+t_{2}+t_{s}+t_{4}. \tag{1}\]  
Establish the motion equation of the inertial mass at the input end as:  
\[J_{r}\frac{d\omega_{r}}{dt}=T_{f}. \tag{2}\]  
The synchronization time:  
\[t_{s}=\int_{\omega_{e}}^{\omega_{c}}\frac{J_{r}}{T_{f}}d\omega_{r}; \tag{3}\]  
\[T_{f}=\frac{F_{s}\mu R}{\sin\alpha}; \tag{4}\]  
where \(T_{f}\) is the friction torque, N-m; \(\mu\) is the friction coefficient; \(F_{s}\) is the axial thrust, N; \(R\) is the average radius of the friction cone, m; \(J\), is the rotational inertia of the synchronizer's input, Kg-m\({}^{2}\); \(\omega_{r}\) is the angular speed of the synchronizer's input, r/min; \(\omega_{c}\) is the angular speed of the synchronizer's output, r/min; \(\omega_{e}\) is the speed of the motor's input shaft, which is also the initial angular speed of synchronizer's input, r/min; \(\alpha\) is half the cone angle of the friction cone, rad.
# High Q-Factor Resonance in a Symmetric Array of All-Dielectric Bars
## 2 Simple Silicon Rod Arrays with High Q Resonance
As the Q value of the ER is more sensitive to the rod's length, the electric and magnetic field enhancement at the wavelength of the ER and the coupling of adjacent rods in the \(y\)-direction with respect to the rod's length are calculated and shown in Figure 6. It is clearly to see the typical TE\({}_{12\delta}\) resonator mode inside the rectangular rods and mode hybridization in the surrounding rods. And the magnetic field is well confined inside the rods, while the electric energy is mainly distributed both inside and surrounding the rods. When the rod's length increases from 680 nm to 840 nm, the electric field enhancement factors both inside the rods (zone 1 in Figure 6) and surrounding the rods (zone 2 in Figure 6) increases exponentially as shown in Figure 7. The field enhancement factors in zone 1 and 2 can reach 48.5 and 67.5, respectively, which are comparable to plasmonic nanostructures [36]. Moreover, as the rod length increases, the gap between adjacent rods in the \(y\)-direction decreases, the local field coupling between the adjacent rods becomes stronger, leading to the depression of resonance radiation into free space, hence the Q value of the resonance is improved. However, due to the weak mode coupling between the neighboring rods in the \(x\)-direction, the decrease of the distance will not improve the resonance Q.  
Figure 4: (**a**,**c**): Transmission spectra of the metasurface as a function of the rod’s length \(a\) (\(b\) = 300 nm) and width \(b\) (\(a\) = 800 nm), respectively. (**b**,**d**): The wavelength dependence of electrical resonance (ER) and magnetic response (MR) on the rod’s length \(a\) and width \(b\), respectively. The rod’s height is fixed at 200 nm. Figure 5: Calculated Q-factors of ER as a function of the rod’s length \(a\) for \(b\) = 260 nm and 300 nm when \(h\) = 200 nm. Figure 1:0 Simulated electric (Ex) and magnetic field (Hz) at ER (normalized to the incident field amplitude E0 and H0) at the \(x\)-\(y\) plane bisecting the nanobars when rod length \(a\) = 680 nm, 760 nm, and 840 nm.
## 4 Results
### _eTL_ and _iTL_ Variables According to the Game Situation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Load** & **Variable** & **Game Situation** & \(M\) & \(SD\) & \(\textbf{X}^{2}\) & \(p\) & \(E^{2}R\) \\ \hline HRmax & No opposition & 167.13 & 25.56 & 168.563 & 0.00 * & 0.12 \\  & Individual game & 174.55 & 17.71 & & & & \\  & Inequality SSG & 176.02 & 23.01 & & & & \\  & Equality SSG & 184.25 & 25.22 & & & & \\  & Full game & 189.40 & 24.71 & & & & \\ \hline HRavg & No opposition & 145.17 & 23.21 & 179.838 & 0.00 * & 0.13 \\  & Individual game & 147.49 & 17.38 & & & & \\  & Inequality SSG & 151.82 & 22.70 & & & & \\  & Equality SSG & 162.96 & 23.96 & & & & \\  & Full game & 162.29 & 24.79 & & & & \\ \hline
50–60\% HR & No opposition & 1.65 & 6.40 & 55.918 & 0.00 * & 0.04 \\  & Individual game & 2.98 & 8.95 & & & & \\  & Inequality SSG & 4.12 & 12.86 & & & & \\  & Equality SSG & 0.78 & 5.00 & & & & \\  & Full game & 3.74 & 8.87 & & & & \\ \hline
60–70\% HR & No opposition & 12.88 & 20.31 & 110.466 & 0.00 * & 0.08 \\  & Individual game & 14.14 & 18.81 & & & & \\  & Inequality SSG & 13.50 & 19.68 & & & & \\  & Equality SSG & 5.90 & 16.01 & & & & \\  & Full game & 9.39 & 10.84 & & & & \\ \hline
70–80\% HR & No opposition & 27.08 & 24.09 & 133.190 & 0.00 * & 0.09 \\  & Individual game & 27.54 & 19.90 & & & & \\  & Inequality SSG & 22.42 & 19.66 & & & & \\  & Equality SSG & 13.39 & 17.77 & & & & \\  & Full game & 13.74 & 11.05 & & & & \\ \hline
80–90\% HR & No opposition & 27.95 & 23.18 & 39.114 & 0.00 * & 0.03 \\  & Individual game & 28.39 & 19.18 & & & & \\  & Inequality SSG & 22.22 & 17.21 & & & & \\  & Equality SSG & 21.05 & 18.08 & & & & \\  & Full game & 18.08 & 15.01 & & & & \\ \hline
90–95\% HR & No opposition & 10.77 & 13.93 & 20.782 & 0.00 * & 0.01 \\  & Individual game & 11.16 & 11.32 & & & & \\  & Inequality SSG & 10.68 & 11.06 & & & & \\  & Equality SSG & 12.99 & 11.96 & & & & \\  & Full game & 9.35 & 11.15 & & & & \\ \hline \hline \(>\)95\% HR & No opposition & 15.02 & 25.23 & 198.541 & 0.00 * & 0.14 \\  & Individual game & 14.84 & 20.89 & & & \\  & Inequality SSG & 25.91 & 30.83 & & & \\  & Equality SSG & 43.83 & 33.60 & & & \\  & Full game & 43.31 & 32.50 & & & \\ \hline \hline \end{tabular} Note: \(M\) = mean; \(SD\) = standard deviation; \(X^{2}\) = Kruskal–Wallis H test; \(E^{2}R\) = epsilon squared coefficient; eTL = external load; Acc = acceleration; Dec = deceleration; PL = Player Load; iTL = internal load; HR = Heart Rate; min = minute; max = maximum; avg = average; SSG = Small-Sided Game. * \(p\) \(<\) 0.05.  
\end{table} 
Table 4: _Cont._Figure 6: Multiple pairwise comparisons of the PL/min and HRavg variables according to the game situation. Note: eTL = external load; PL = Player Load; min = minute; iTL = internal load; HR = Heart Rate; SSG = Small-Sided Game. (**a**) eTL (PL/min) and (**b**) iTL (HRavg).
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 3 Rosenbrock-Algorithm-Based Beam Training
### Rosenbrock Search
#### 3.2.2 Pattern Moving  
From (8) and (6), we may have  
\[s^{(k+1)}=s^{(k)}+\sum_{i=1}^{n}round\!\left(\lambda_{i}d^{(i)}\right) \tag{9}\]  
where \(\lambda_{i}\) represents the cumulative moving step along \(d^{(i)}\).  
It is observed that \(p\stackrel{{\Delta}}{{=}}s^{(k+1)}-s^{(k)}\) may get close to the ascent direction, so during the next round of probes, the new constructed direction should take this ascent direction into consideration. The new constructed search directions can be formed from \(s^{(k+1)}-s^{(k)}\) and further orthogonalized by utilizing the Gram-Schmidt orthogonalization procedure. Then, the algorithm alternates between the probe round and pattern round, until the relative change in the objective function values is below a pre-specified threshold \(\eta\), i.e., \(\left\|s^{(k+1)}-s^{(k)}\right\|\leq\eta\)[21].  
Based on the above elaborations, the pseudo-code for the Rosenbrock search is provided in Algorithm 1.  
current temperature \(T\), the probability of a decrease in energy amplitude (i.e., \(\Delta E=E(i)-E(i+1)>0\)) can be defined by  
\[P(\Delta E,\ T)=\frac{\exp(-E(i)/kT)}{\exp(-E(i)/kT)+\exp(-E(i+1)/kT)}\approx\exp (-\Delta E/kT) \tag{10}\]  
where \(k\) denotes the Boltzmann's constant. If \(\Delta E>0\), the state transition from \(x^{(i)}\) to \(x^{(i+1)}\) is accepted. Otherwise, if \(\Delta E<0\), the new solution \(x^{(i+1)}\) may be allowed to use the probability returned by (10). The state transition happens if the following condition is fulfilled:  
\[P(\Delta E,\ T)\geq\gamma \tag{11}\]  
where \(\gamma\) denotes a random number between 0 and 1, i.e., \(\gamma\sim U(0,\ 1]\).  
In addition to the high initial temperature \(T_{0}\), a gradual reduction of temperature is also required as the search progresses. So, we have:  
\[T(k)=T_{0}\times\kappa^{k} \tag{12}\]  
Here, \(k\) is the updating iterator. The decay factor \(\kappa\) satisfies \(0<\kappa 1\).
## 3 Results and Discussion  
### Phenolic Content Estimation Assays (TPC, TTC, TCT)  
The phenolic contents in the coffee samples were estimated based on the results of three phenolic assays, which are TPC, TFC, and TTC, as shown in Table 1. Overall, the amount of free phenolic compounds was higher than the amount of bound phenolic compounds for all samples, and fermented coffee samples showed a significantly higher phenolic content than unfermented coffee samples (\(p\) < 0.05). The free phenolic content in fermented Geisha and Bourbon coffee beans was found to be 33.52 mg and 29.95 mg GAE/g, respectively, which was higher than that in the unfermented beans (29.19 mg and 23.82 mg GAE/g, respectively). This increase in total phenolic content aligns with [PERSON] et al. [21], where all groups of fermented green coffee beans exhibited higher phenolic content compared to the unfermented control group. The increased phenolic content after fermentation can be attributed to the liberation of bound phenolic compounds from the coffee beans [22]. Coffee beans contain various groups of phenolic compounds, with chlorogenic acids (CGAs) being the major phenolic components in coffee brews. These CGAs are commonly found in the conjugated form