esa-sceva/satcom-chunk-collection huggingface dataset:
A representative industrial study [17] benchmarks several 802.11 generations at different line-of-sight distances but does not consider the richer interference patterns present in real factory settings. These gaps underscore the need for broader, multi-scenario measurement campaigns that evaluate modern Wi-Fi technologies across varied bands, channel widths, and interference conditions. To overcome these gaps and enhance the diversity of scenarios tested for Wi-Fi technologies, this article examines the performance of the latest versions of the IEEE 802.11 standard in different relevant environments, showcasing the technology's evolutionary advancements and adaptability within the context of Industry 5.0. To evaluate the capabilities of these Wi-Fi technologies, typical network parameters such as Round-Trip Time (RTT),peak throughput (THR), packet loss (PL), and jitter are measured. Furthermore, the evaluation of these parameters is extended to two types of traffic with different characteristics. The paper is organised as follows. Section 2 outlines the technical advances and theoretical performance of recent IEEE 802.11 standards. Sections 3 and 4 describe the experimental setup, the evaluated configurations, and the three deployment environments. Sections 5 and 6 present and analyse the results following the same structure. Finally, Section 7 concludes the paper and outlines directions for future work.
# Shedding Light on the Volatile Composition of _Broa_, a Traditional Portuguese Maize Bread
## 3 Results and Discussion
### Characterization of the Volatile Composition of Broas
Similar to maize flours, strong positive correlations were found among lipid oxidation products, such as aldehydes, alcohols and some ketones [20]. Positive correlations (R > 0.59, \(p\) < 0.05) were also detected among compounds formed during baking, such as methylpyrazine, 5-methylfurral, furfurylpyrrole and 2-furanmethanol, and among volatiles which may have been originated during fermentation, namely hexanoic acid, benzyl alcohol, ethyl hexanoate, 1-hexyl acetate, ethyl octanoate, \(\gamma\)-nonlactone, 3-octen-2-one and 2-methyl-3-octanone. Positive correlations were also commonly found among these volatiles and several alcohols, such as isopentanol, 1-pentanol, 1-hexanol and 1-heptanol, suggesting that these compounds have also been originated by bacteria or yeast fermentation. Conversely, significant negative correlations were found among some volatiles derived from fermentation, such as 2-methyl-1-butanol, phenylethyl alcohol and 2-methyl-3-octanone, with several lipid oxidation products, such as aldehydes (hexanal, 4-heptenal (_Z_), heptanal, benzaldehyde, nonanal and decanal) and hydrocarbons (3-ethyl-2-methyl-1,3-hexadiene, 2,4-dimethyl-1-decene, dodecane and tetradecane). Other authors have reported similar negative correlations in other food products [73]. One explanation for these correlations is the lower amylase and yeast activities in some flours, which consequently provided a higher amount of active oxygen for oxidation, contributing to the increase in lipid oxidation reactions [30]. Alternatively, a higher yeast activity may have promoted the metabolization of aldehydes to the corresponding alcohols. [PERSON] et al. [28] have reported that _p_-vinylguaiacol can significantly reduce the generation of Maillard-type aroma compounds, but only a modest negative correlation was found between this compound and methylpyrazine in _bros_ (R = -0.587, \(p\) < 0.05), whereas no correlations were found between this compound and other Maillard derivatives (Supplementary Table S6). The volatile composition of maize kernels plays a major role in overall end-product quality [40]. Therefore, in order to understand the influence of maize volatiles on _bros_' volatile composition, the volatile compounds detected in maize flours and _bros_ were correlated (Supplementary Table S7). In general, higher amounts of lipid oxidation products were found in _bros_ prepared from maize flours that presented higher contents in these compounds. For instance, 1-octen-3-ol was detected in higher concentrations in _bros_ produced from maize flours with higher contents in 1-octanol (R = 0.804, \(p\) < 0.01) and maize flours with higher contents in heptan originated _bros_ with higher contents in 3-ethyl-2-methyl-1,3-hexadiene (R = 0.818, \(p\) < 0.01).
## 4 Conclusions  
This study conducts numerical simulation by ZDES based on the k-\(\omega\) SST model to investigate the transonic shock buffet phenomenon of the OAT15A supercritical airfoil under transonic conditions. The main conclusions can be drawn as follows:  
(1) ZDES is suitable for fine numerical simulation of the transonic shock buffet, effectively capturing the phenomenon of a shock wave self-sustained buffet. Through the analysis of the evolution of the complex flow field during one cycle of the transonic shock buffet, it is observed that the motion of shock waves and the resulting flow separation and reattachment exhibit periodic characteristics, leading to oscillations in the overall aerodynamic forces. This provides further insights into the formation mechanism of a shock wave self-sustained buffet.  
(2) In comparison with DDES and IDDES, the power spectral density of the lift coefficient suggests that ZDES can better capture the low-frequency characteristics of the transonic shock buffet. Analysis of the pressure distribution and pressure fluctuation distribution indicates that ZDES predicts the average shock wave position and the range of shock wave motion in better agreement with the experimental results. However, the maximum pressure fluctuation level is slightly lower, suggesting a possible limitation due to insufficient grid density. ZDES also exhibits better agreement with experimental results in predicting the average streamwise velocity.  
(3) It has been found that the instability generated by flow separation interacts with the trailing edge during the periodic evolution of the instantaneous wave field. This interaction affects the intensity of pressure waves radiated upstream from the trailing edge, and the intensity of these pressure waves determines the direction of shock movement, which is the key mechanism behind the self-excited transonic shock buffet.  
Conceptualization, T.L.; methodology, X.C.; software, X.C.; validation, T.L. and X.C.; formal analysis, T.L.; investigation, T.L.; resources, J.L.; data curation, Z.T.; writing--original draft preparation, X.C.; writing--review and editing, X.C. and Z.T.; visualization, T.L.; supervision, X.C.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
Figure 15: The power spectral density of the lift coefficient (ZDES).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **DDES** & **IDDES** & **ZDES** & **EXP** \\ \hline Frequency & 80.5 Hz & 78 Hz & 67 Hz & 69 Hz \\ \hline \end{tabular}
\end{table}
Table 3: The oscillation frequency of shock waves obtained by different methods.  
**Funding:** This research is supported by the National Natural Science Foundation of China (Grant No. 92052203).  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Model and Methods
### Bayesian Hierarchical Modelling Framework  
A probabilistic framework is a common modelling choice for UQ, since it accounts for randomness and uncertainty in a systematic manner. Based on the physically derived model in (2), we consider a Bayesian hierarchical (multi-level) modelling approach to accommodate different types of uncertainty in the input parameters.
# Axial Compression Behavior of Steel Angle-Corrugated Steel Plate-Confined Concrete Columns
## 4 Finite Element Analysis
### Parametric Analysis  
In order to evaluate the influence of different parameters on the axial compressive behavior of the SA-CS column, a parametric analysis was carried out based on the validated model. The variation included the compressive strength of concrete (\(f_{\text{cu}}\)), the yield strength (\(f_{\text{y1}}\)) and thickness (\(t_{1}\)) of the corrugated steel plate, the yield strength (\(f_{\text{y2}}\)) and thickness (\(t_{2}\)) of the steel angle, and the wave height of the corrugated steel plate (\(h\)). The values of the parameters are listed in Table 2. To ensure the use of a single variable in the parametric analysis, the width of all FE models was 280 mm.  
Figure 12: Comparison between test and FE.  
Figure 13: Comparison of FE and test load–axial displacement curves.  
Figure 14 illustrates the influence of the parameters on the load-displacement curves of the specimens and the core concrete's strength index (\(I_{\text{s}}\)). Figure 14a shows that the ultimate bearing capacity (\(N_{\text{u}}\)) increased with the improvement of \(f_{\text{cu}}\). Still, the results regarding \(I_{\text{s}}\) and ductility were opposite, indicating that the confinement effect of the core concrete provided by the outer steel skeleton decreased. Especially when \(f_{\text{cu}}\) was taken as 60 MPa, \(I_{\text{s}}\) was just 1.008 and the core concrete was unconfined. Figure 14b,c show that \(N_{\text{u}}\), \(\mu\), and \(I_{\text{s}}\) increased with the improvement of \(f_{\text{y1}}\) or \(t_{1}\), which demonstrated that the corrugated steel plate served primarily to confine the core concrete. In addition, with respect to Figure 14d,e, the general shapes of the load-displacement curves, including the initial stiffness and softening region, were similar. \(N_{\text{u}}\) and \(I_{\text{s}}\) were not obviously affected by the improvement of \(f_{\text{y2}}\) or \(t_{2}\). It can be inferred that the steel angle had little effect on confining concrete and only played a role in bearing the load. Figure 14f shows the influence of \(h\) on the load-displacement curves and \(I_{\text{s}}\). \(N_{\text{u}}\) increased with the improvement of \(h\), but for \(I_{\text{s}}\), the variation trend was not monotonous: it increased first and then decreased. In order to explore the wave heights that could provide the most effective confinement of core concrete, the wave heights between 25 mm and 30 mm were divided in detail, and an FE simulation was carried out. The results are shown in Figure 15. It can be seen  
Figure 14: Results of FE parametric analysis.  
Figure 15: Influence of wave height of corrugated steel plate on \(N_{\text{u}}\) and \(I_{\text{s}}\).
# BIMO: Bootstrap Inter-Intra Modality at Once Unsupervised Learning for Multivariate Time Series
## 3 Results and Discussion
### Univariate Time Series
**Inter-Intra Modality Representation Ablation:** We compared performance depending on the combination of auxiliary networks based on the average rank according to the accuracy results on the UCR datasets. We used a single auxiliary network, e.g., an inter-auxiliary or intra-auxiliary network, and multiple auxiliary networks, e.g., inter-auxiliary and intra-auxiliary networks. As shown in Table 3, we compared the performance in terms of the average rank score. More detailed overall accuracy results are shown in Table 4. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{2}{c}{**Single**} & \multicolumn{3}{c}{**Plural**} \\ \hline
**Dataset** & _Inter_ & _Intra_ & _Inter and Intra_ & _Inter_\(\mapsto\)_Intra_ & _Intra_\(\mapsto\)_Inter_ \\  & _Inter_ & _Intra_ & _Inter and Intra_ & _Inter_\(\mapsto\)_Intra_ & **(BIMO)** \\ \hline Adiac & **0.778** & 0.693 & 0.729 & 0.642 & 0.760 \\ ArrowHead & **0.831** & 0.767 & 0.826 & 0.785 & 0.814 \\ Beef & 0.750 & 0.786 & 0.786 & **0.821** & 0.800 \\ BeetleFly & 0.850 & 0.850 & **0.900** & 0.850 & 0.850 \\ BirdChicken & 0.850 & **0.900** & 0.800 & 0.797 & **0.900** \\ Car & **0.917** & 0.850 & 0.883 & 0.983 & 0.916 \\ CBF & 0.990 & 0.993 & 0.996 & 0.986 & **0.998** \\ ChlCon & 0.613 & 0.627 & 0.597 & **0.733** & 0.635 \\ CinCEGTorso & 0.745 & 0.737 & 0.766 & **1.000** & 0.757 \\ Coffee & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** \\ Computers & 0.633 & 0.621 & **0.681** & 0.625 & **0.681** \\ CricketX & **0.771** & 0.629 & 0.683 & 0.649 & 0.750 \\ CricketY & 0.696 & 0.585 & 0.686 & 0.652 & **0.716** \\ CricketZ & 0.750 & 0.626 & 0.706 & 0.670 & **0.758** \\ DiaSizRed & 0.961 & 0.980 & 0.964 & **0.984** & 0.977 \\ DisPhOutAgeGroup & 0.735 & 0.699 & 0.735 & 0.721 & **0.786** \\ DisPhazOutCorrect & **0.772** & **0.772** & 0.761 & 0.750 & 0.743 \\ DistalPhalanxTW & **0.721** & 0.669 & 0.669 & 0.713 & 0.684 \\ Earthquakes & 0.750 & 0.735 & 0.735 & 0.706 & **0.765** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy scores depending on the combination of auxiliary networks for the first and recent UCR datasets: using a single auxiliary network (_Inter_ or _Intra_) and plural auxiliary networks (_Inter_ and _Intra_, _Inter_\(\mapsto\)_Intra_, _Intra_\(\mapsto\)_Inter_). Bold text represents the best accuracy, and the underlined text represents the second-best accuracy. Figure 2: Average rank diagram of BIMO, existing SOTA unsupervised models (USRL, DTW), and supervised models (PF, BOSS, HIVE-COTE, ITime) for the UCR datasets. The average rank means the average of the top ranking results of a model. The black lines indicate an unsupervised models, and dotted lines represent supervised models.
# Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review
## 4 Inversion Analysis of Supporting Load  
In tunnel and underground space engineering, the research on structural load mainly focuses on the analysis of supporting structure load during excavation. In this kind of study, the inversion theory mainly considers the influence of stratum pressure (i.e., the load borne in the process of lining structure contacting with stratum). According to the measured information of displacement and stress, the load acting on the supporting structure is inverted, and the distribution law is further explored.
## 5 Conclusions  
This study was aimed at evaluating the feasibility of GONF as an addition in GONF-combined cement composites. To identify the optimum content of GONF to increase the mechanical properties of GONF-combined paste and concrete, different percentages of GONF were added in cement mix. To investigate the effect of mix designs, two different mix design methods were employed (Dry- and Wet-mix design methods). In addition, to verify the practicality of Dry-mix design for GONF-combined concrete, PCC specimens which contain different percentages of dry GONF powder were made,  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Shots** & **C (\%)** & **O (\%)** & **Al (\%)** & **Si (\%)** & **S (\%)** & **Ca (\%)** & **Pd (\%)** & **Au (\%)** & **Total (\%)** & **Probable Compounds** \\ \hline
1(Area) & 12.43 & 30.05 & — & 3.17 & — & 50.93 & — & 3.42 & & C-S-H + GO \\
2(Area) & 15.56 & 39.99 & 0.86 & 10.15 & — & 25.74 & 4.56 & 3.15 & & Jeninite + GO \\
3(Point) & — & 13.70 & 2.58 & 9.23 & 5.47 & 69.01 & — & — & 100 & Ettrighte \\
4(Point) & — & 7.29 & 1.58 & 12.91 & 2.35 & 75.87 & — & — & & Ettrighte \\
5(Point) & 16.94 & 39.34 & — & 8.38 & — & 26.00 & 5.62 & 3.72 & & C-S-H + GO \\ \hline \hline \end{tabular}
\end{table}
Table 6: Energy dispersive spectroscopy (EDS) analysis on GONF-cement composite (from SEM image of Figure 9(b)).  
Figure 10: The images of SEM analysis (GONF-combined cement paste with 0.05% GONF at 7 days). (**a**) SEM image with 5000 X magnification; (**b**) SEM image with 10 kX magnification.  
cured and tested. A series of laboratory tests were conducted to evaluate mechanical and chemical properties of GONF-combined PCC specimens. The following conclusions have been made:  
* It was found that mechanical properties of all GONF-combined paste and concrete have been improved compared to the control specimens. According to compressive and flexural strength tests, highest strengths are observed at GONF of 0.05% with GON-combined paste and at GONF of 0.01% with GONF-combined concrete; thus, we conclude that 0.05% and 0.01% of GONF are an optimum content in cement paste and concrete.
* Wet-mix designs exhibit higher compressive and flexural strengths than Dry-mix designs. Well-dispersion of GONF in cement paste may contribute the higher strengths. However, Dry-mix design where the GONF is mixed with cement as dry power can be also competitive from a practical viewpoint.
* Petrographic analysis (SEM/EDS) identifies GONFs inside products of cement hydration (surrounded by C-S-H), confirming GONF's a potential role as nano-reinforcing and nano-filling effects.
* To actualize applying this new nanomaterial in concrete industry, Dry-mix design method can be an alternative mix design method.  
This work is supported by Korea Agency for Infrastructure Technology Advancement grant funded by Ministry of Land, Infrastructure and Transport (Project Number: 17 CTAP-C129720-01).  
[PERSON] and [PERSON] conceived the whole procedure of this study. [PERSON] and [PERSON] designed, performed and analyzed the mechanical and petrographic tests.  
The authors declare no conflict of interest.
## 5 Conclusions
* The evaluation result of the system failure probability obtained by considering multiple failure criteria was obtained via weighted summation of the system failure probability of each failure criterion, and the calculation result was \(5.418\times 10^{-5}\) (\(P_{f}\)), which is between \(1.099\times 10^{-4}\) (\(P_{f_{1}}\)) and \(1.049\times 10^{-5}\) (\(P_{f_{1}}\)). It shows that the method proposed in this paper can provide a more comprehensive and reasonable system reliability evaluation result, and avoid the ambiguity caused by the selection of a single failure criterion. Conceptualization, H.L. and G.T. ; formal analysis, H.L. and G.T. ; funding acquisition, H.L. and G.T. ; investigation, G.T., X.W., and X.H. ; methodology, X.W. and G.T. ; software, X.W. and X.H. ; writing--original draft, H.L. and X.W. ; writing--review and editing, G.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant numbers 51478203 and 51978309), Science and Technology Project of Education Department of Jilin Province (grant number JKH20190150 KJ), Scientific and Technological Project of Science and Technology Department of Jilin Province (grant number 2019030052 SF), and Budget Capital Construction Fund of Jilin Province (grant number 2019C041-5). The authors would like to thank the anonymous reviewers for their constructive suggestions and comments toward improving the quality of the paper. The authors declare no conflict of interest.
## 2 Related Work
### Quantification of Privacy Loss  
Privacy quantification is used to measure the level of privacy protection of a system or algorithm [34]. Privacy quantification involves a wide range of mathematical concepts and metrics that serve as the foundation for measuring and evaluating privacy. Mutual information, entropy, and \((\varepsilon,\delta)\)-DP are examples of these concepts [35; 36; 37]. Other entropy concepts, such as [PERSON]'s min-entropy, are used in privacy quantification to represent different types of attacks and information leakage scenarios [38].  
The most well-known formal definition of differential privacy (DP) is the [PERSON]'s formula [39], which is as in Equation (1).  
A randomized algorithm \(\mathcal{M}\) with domain \(\mathbb{N}^{|\mathcal{X}|}\) is \((\varepsilon,\delta)\)-differentially private if \(\forall S\subseteq\text{Range}(\mathcal{M})\) and \(\forall x,y\in\mathbb{N}^{|\mathcal{X}|}\) such that \(\|x-y\|_{1}\leq 1\):  
\[\Pr[\mathcal{M}(x)\in S]\leq\exp(\varepsilon)\Pr[\mathcal{M}(y)\in S]+\delta \tag{1}\]  
where  
\(\mathcal{M}\) is a random algorithm (or also called a query mechanism);  
\(S\) is the set of possible outcomes of \(\mathcal{M}\);  
Epsilon (\(\varepsilon\)) is called the privacy budget and presents the maximum distance between \(\mathcal{M}(x)\) and \(\mathcal{M}(y)\);  
Delta (\(\delta\)) is the probability that information is accidentally leaked.  
If \(\delta=0\), we say that \(\mathcal{M}\) is \(\varepsilon\)-differentially private or, in short, \((\varepsilon,0)\)-DP or \(\varepsilon\)-DP. Otherwise, we say that it is \((\varepsilon,\delta)\)-differentially private or, in short, \((\varepsilon,\delta)\)-DP.  
\((\varepsilon,0)\)-DP controls the amount of privacy protection provided, while \((\varepsilon,\delta)\)-DP adds the second layer of privacy protection, which represents the maximum probability of privacy violation. The idea is that including or excluding an individual's data should not have a major impact on the outcome of a query or expose specific information about that individual. \((\varepsilon,\delta)\)-DP offers a quantitative measure of privacy guarantees, and denotes the level of privacy protection. \((\varepsilon,\delta)\)-DP guarantees that the information disclosed about individuals remains within acceptable limits by imposing specific constraints on data release techniques, such as adding properly adjusted noise to the query results.  
Various studies have been conducted on the use of DP to protect privacy in FL systems. However, practical evaluation in the real world is still limited. The work in this study addresses the limitations of previous research by conducting a thorough experimental evaluation of DPFL on two real-world data sets from two different organizations in Afghanistan. We study the robustness of DPFL to adversarial attacks and evaluate its performance and implications for the privacy protection process.
## 2 Background and Related Work
### Multi-Stakeholder Recommender Systems (MSRS)  
The MSRS paradigm has gained significant popularity in the last few years. This paradigm, in contrast to the traditional one that puts the user (recommendation receiver) in the center of attention, also takes into consideration other stakeholders of recommender systems [5; 8] such as item providers, the recommender system operator, and society in general. In this context, several recent studies focus on this paradigm. Specifically, [PERSON]. [5; 8] identify the need for examining recommender systems from the different stakeholders' perspective and introduce the multi-stakeholder recommender systems paradigm. On the other hand, [PERSON] et al. [9] investigated multi-sided fairness in multi-stakeholder recommendations and how different fairness concerns can be introduced in such systems. Moreover, [PERSON] et al. [10] analyzed the ethical aspects of MSRS. Furthermore, [PERSON]. [11; 12] review the existing case studies, methods, and challenges, and propose new research directions for MSRS. Last but not least, [PERSON]. [13; 14] present applications of the MSRS paradigm. The first introduces provider constraints to the multi-stakeholder recommendation problem and formulates it as an integer programming optimization model that is solved using an approximation and can achieve satisfactory results in real use cases. On the other hand, in the second article the authors developed a multi-objective binary integer programming model to allocate sponsored recommendations. As a next step, they present an algorithm to solve the problem in a computationally efficient way. The proposed approach was applied to a real use case with good results and is easily applicable to existing recommender systems as it is applied as a form of postprocessing.  
Of course, in the publication at hand, we are not examining MSRS in general; instead, we focus on the metrics of coverage and diversity across provider items and address the need for fairness across the latter. Therefore, it is worth presenting the different definitions of coverage, diversity, and fairness.