esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of the Phase Fraction  
As already seen in Figure 6, the DSD and the Sauter mean diameters are significantly affected by the phase fraction. The volume distributions in Figure 7a shifts towards larger droplets and becomes slightly broader with higher phase fractions. This effect is more pronounced for lower phase fractions than for higher ones. The experimental data for the phase fractions \(\varphi\) = 30 vol.-% and \(\varphi\) = 50 vol.-% in Figure 7a are almost identical. This indicates that the influence of the phase fraction on the DSD cannot be adequately represented by a linear function and that more complex functions have to be used. This effect was observed with all investigations within the typical error range of \(+/-20\)\(\upmu\)m. The Sauter mean diameter as a function of the phase fraction is depicted in Figure 7b. It decreases with increasing impeller speed. This is expected and reasonable since the mean energy dissipation increases with the impeller speed. An increase for the Sauter mean diameters is found for increasing phase fractions due to the higher collision rate of droplets.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Coefficients**} & \multicolumn{4}{c}{**Confidence Intervals**} \\ \hline \multirow{2}{*}{Values Equation (8)} & \(C_{4}\) & \(C_{5}\) & \(C_{9}\) & \(R^{2}\) & \(C_{4}\) & \(C_{5}\) & \(C_{9}\) \\  & 0.1601 & 1.9190 & \(-\)0.6456 & 0.9503 & 0.1113 to 0.2089 & 1.6180 to 2.2200 & \(-\)0.6948 to \(-\)0.5964 \\ \hline \multirow{2}{*}{Values Equation (9)} & \(C_{6}\) & \(C_{7}\) & \(C_{9}\) & \(R^{2}\) & \(C_{6}\) & \(C_{7}\) & \(C_{9}\) \\  & 0.4283 & 0.2933 & \(-\)0.6475 & 0.9719 & 0.3306 to 0.5260 & 0.2693 to 0.3173 & \(-\)0.6845 to \(-\)0.6105 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients and confidence intervals for Equation (8) [50] and Equation (9) [51].  
Figure 6: Experimental Sauter mean diameter (experiment 1: blue solid symbols, repetition experiment 2: red hollow symbols) at different rpm and phase fractions (dotted lines are a guide to the eye).
# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
## 4 Results
The Progressive Transformers method for end-to-end sign language production demonstrates a marginal improvement with a BLEU score of 55.65%, yet it is hindered by high computational costs, risks of overfitting to specific datasets, challenges in generalising to diverse sign languages and dialects, and difficulties in aligning gestures with variations in textual input. In contrast, the proposed method utilising a Transformer and Seq2 seq model with a parallel corpus demonstrates significant improvement, achieving a BLEU score of 77.50%, which underscores its effectiveness, particularly for KSL (See Table 5). While this manuscript emphasises the innovation of incorporating specialised rules and a custom parser for KSL, further elaboration is required on the impact of these adaptations on the model's performance. Addressing these challenges necessitates an exploration of linguistic properties unique to KSL and the potential enhancement of the model with additional grammatical normalization strategies or hybrid approaches to improve fluency and coherence in translations. Figure 5: (a,b) Learning Process (Loss Function). Figure 6: BLEU Score. In the course of the present experiments, it was found that sentences comprising up to 42 words presented a considerable challenge to CNN models [9] due to their inability to capture long-range dependencies. Similarly, LSTMs [10], while demonstrating aptitude for capturing local patterns, were unable to grasp the global context of such sentences. By contrast, Transformers demonstrated superior performance, efficiently processing long sentences by means of their attention mechanism, which dynamically focuses on relevant parts of the input regardless of sentence length (See Table 6).
## 4 Proposed Protocol
### Cluster Maintenance  
To reduce energy consumption and balance the load, this paper adopts the on-demand maintenance mechanism. The CHs consume too much energy to collect and transmit data to the BS, and the clusters nearer to the BS consume energy too quickly, so the cluster maintenance phase is considered as one of the important phases for prolonging the network lifetime. Each selected CH remains in its position for a certain number of rounds. Once its remaining energy is below the threshold \((E(N_{i})<E_{threshold})\), the CH announces the node with the second-largest probability of becoming a new CH. Based on the saved chance values, the CH shift can be performed every time with only a small control packet. Moreover, when a node only functions as a CH in a round, it transmits a small control packet to its neighboring CHs instead of its CMs. Additionally, the data packet will be forwarded by the neighbor CHs until all CHs receive it. The BS will re-initialize the FIS and reselect the CHs on demand when all CHs perform a shift operation or if a CH has exhausted its energy. Then, the quantum annealing algorithm is used to obtain the optimal routing path from CHs to the BS.
PbAcE accommodates the diverse alcohols moieties of substrates in a substrate binding pocket, designated as Figure S1 (Figure 1a); however, it can only fit short acyl moieties in the other substrate-binding pocket, designated as S0. Among the three tertiary alcohol esters examined in the previous study, namely _tert_-butyl, linalyl, and \(\alpha\)-terpinyl acetates, PbAcE exhibited higher hydrolytic activity to _tert_-butyl acetate than the other two [14]. The enzyme also showed higher activity to 7-ACA and cefotaxime than to CPC. Enzymatic CPC deacetylation by PbAcE to deacetyl-CPC is an initial production step for deacetyl 7-ACA, which is a key pharmaceutical intermediate for semi-synthetic \(\beta\)-lactam antibiotic production [20]. Here, we engineered the S1 substrate-binding pocket of PbAcE to improve its activity toward tertiary alcohols, such as linalyl and \(\alpha\)-terpinyl acetates. We covalently docked four ligands, namely _tert_-butyl acetate, linalyl acetate, \(\alpha\)-terpinyl acetate, and glyceryl tribuyrate, to the Ser 185 of PbAcE using HCovDock [21; 22] to map out the residues interacting with substrates. Furthermore, HotSpot Wizard 3.1 was used to find functional residues in AXE with high mutability [23]. Lys91, His93, and Tyr182 were selected for site-saturation mutagenesis (SSM). Positive mutants for each residue were screened from single mutant libraries using a colorimetric assay method: Ala, His, and Ser for Lys91 residue; Pro and Asp for His93; and Glu, Thr, Val, Met, and Trp for Tyr182. The activity of the mutant enzymes towards linalyl acetate and \(\alpha\)-terpinyl acetates increased by 10-30% compared to that of the wild-type PbAcE. By combining the best single mutants from each residue, we sequentially generated double and triple mutants. However, no synergistic improvements were observed in the double and triple mutants compared to the wild-type and single mutants. Steady-state kinetics of mutant PbAcEs against the _p_NP-C\({}_{2}\) substrate revealed that acetyl binding and catalysis were not significantly affected by the mutations. Our findings may provide insight into engineering the substrate binding pockets of biocatalysts for industrial applications.
## 3 Results  
### Density and Consistency  
The results of tests on the influence of waste raw materials on the density, workability, and air content of the mixes are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mixture ID**} & \multicolumn{5}{c}{**Composition of Mortar Mixtures [kg/m\({}^{3}\)]**} \\ \cline{2-6}  & **Cement** & **Glass** & **River sand** & **PET** & **Water** \\ \hline
05-0 PET & 511.6 & 0.0 & 1534.8 & 0.0 & 255.8 \\
105-0 PET & 460.4 & 51.2 & 1524.1 & 0.0 & 255.8 \\
205-0 PET & 409.1 & 102.3 & 1513.4 & 0.0 & 255.7 \\
05-5 PET & 511.6 & 0.0 & 1458.0 & 40.0 & 255.8 \\
105-5 PET & 460.3 & 51.1 & 1447.3 & 40.0 & 255.7 \\
205-5 PET & 409.1 & 102.3 & 1436.6 & 40.0 & 255.7 \\
05-10 PET & 511.6 & 0.0 & 1381.3 & 79.9 & 255.8 \\
105-10 PET & 460.3 & 51.1 & 1370.9 & 79.9 & 255.7 \\
205-10 PET & 408.9 & 102.2 & 1360.5 & 79.9 & 255.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mortar mix recipes.  
The density of fresh mortar mixes remains unaltered with an increase in the proportion of glass powder, partially substituting cement in the mortars. The reduction in the density of PET mortars is attributed to the substitution of some of the sand with waste aggregate, likely due to the lower density of PET compared to silica sand. The combined utilisation of waste raw materials results in a decrease in the density of mortars fabricated from them compared to a sample without additives. The flow of mortar mixes experiences a minor increase with a rise in the glass powder content and a more substantial increase with a rise in the PET proportion. PET waste, utilised as a 5-10% replacement for fine aggregate, comprises approximately 80% of 2-4 mm particles, distinctly diverging from the grain size of silica sand. Owing to the decrease in the specific surface area of the entire aggregate pile, an augmentation in the flow of mortars constructed with a polymer aggregate is observed. Concurrently, the employment of both waste raw materials culminates in the recorded elevated mortar flows. The air content was the lowest in mortars with 20% glass content and the highest in the case of 10% PET content. Mortars with 10% glass content had comparable air contents to mortars without glass powder.
## 3 Results and Discussion  
### Investigation of 3D-rGOPFH  
#### 3.1.1 Characterization of 3D-rGOPFH  
The morphologies of as-prepared PNT and 3D-rGOPFH were observed under the digital camera, SEM, and TEM. In Figure 2a, PNT possessed tubular form with a length of about 5 \(\upmu\)m and a diameter of about dozens of nanometers. After being self-assembled with GO and Fe\({}_{3}\)O\({}_{4}\) nanoparticles, the well-defined black hydrogel in a cylindrical shape floated on the top of the system. According to figure, only a transparent solution was left at the bottom of the conical flask after the hydrothermal reaction, and no separated Fe\({}_{3}\)O\({}_{4}\) nanoparticles and GO sheets could be elsewhere, suggesting that the ternary system, containing PNT, GO and Fe\({}_{3}\)O\({}_{4}\) nanoparticles, self-assembled adequately (Figure 2b). The 3D-rGOPFH exhibited an interconnected network architecture with uniformly dispersed pores (Figure 2c,d). The PNT was evenly attached on the surface of rGO sheets (Figure 2e); meanwhile, some of PNT were dispersed between rGO sheets, avoiding the stacking or aggregation of rGO sheets in two-dimension magnetic graphene 2D-G-Fe\({}_{3}\)O\({}_{4}\) (Figure 2f).  
Figure 2: (**a–f**) The morphologies of the as-prepared materials were observed using SEM, TEM, and digital camera. (**g**) The nitrogen adsorption-desorption isotherm and pore size distribution analysis of 3D-rGOPFH. (**h**) FT-IR spectra of GO, rGO, PNT, Fe\({}_{3}\)O\({}_{4}\) nanoparticles and 3D-rGOPFH.  
The 3D architecture of 3D-rGOPFH was further validated by using the Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) techniques, respectively. In Figure 2g, the nitrogen adsorption-desorption isotherm corresponding to 3D-rGOPFH displayed the type II hysteresis loop, characteristic to the pores with different pore sizes. The specific surface area and total pore volume of 3D-rGOPFH were determined to be 237 m\({}^{2}\) g\({}^{-1}\) and 0.276 cm\({}^{3}\) g\({}^{-1}\), respectively. The pore size distribution was calculated by density functional theory to be in the range from 1 to 10 nm.  
Figure 2h represents the FT-IR spectra of the fabricated GO, rGO, PNT, Fe\({}_{3}\)O\({}_{4}\) nanoparticles, and 3D-rGOPFH. The main characteristic peaks of rGO (1625 cm\({}^{-1}\)), PNT (1544 cm\({}^{-1}\) and 1455 cm\({}^{-1}\)), and Fe\({}_{3}\)O\({}_{4}\) nanoparticles (574 cm\({}^{-1}\)) were included in the FT-IR spectra of 3D-rGOPFH, suggesting the successful fabrication of target material. Additionally, the successfully fabrication of 3D-rGOPFH was further demonstrated by XRD spectra (Figure S1a).  
The magnetic properties of the as-fabricated 3D-rGOPFH at room temperature were measured using a vibrating sample magnetometer. The magnetic hysteresis loops show that 3D-rGOPFH has a super-paramagnetic property with maximum saturation magnetization (M\({}_{\rm s}\)) at 40.3 emu g\({}^{-1}\) (Figure S1b).
## 3 Results
### Vaccination  
Owners and carers of unowned dogs reported that the majority of dogs were vaccinated against rabies (57.7%). For 53 of these vaccinated dogs, their date of last vaccination was greater than one year and hence their vaccination status was categorised as 'lapsed', resulting in 56.0% of dogs with a current vaccination status.  
Four variables were selected to test for their ability to predict whether a dog was likely to be unvaccinated; gender, age, breed, and confinement method. These variables were selected because they were all immediately apparent by simply observing the dog, as opposed to variables such as source of the dog or prior breeding history of females which would require owner interview. This would potentially allow future vaccination campaign staff to target dogs likely to be unvaccinated based on visible dog characteristics. Prior to running the logistic regression, age was split into four categories: puppy (<4 months), juvenile (4-12 months), adult (1 < 7 years), and old adult (7+ years). Dogs with incomplete data for any of the four variables or vaccination status were removed from the analysis, leaving \(n\) = 5461.  
Logistic regression was used to test both main effects and pairwise interactions to establish the odds of not being vaccinated for each variable and pair of variables. Only one pairwise interaction was found to be significant; Bali dogs allowed to roam freely without confinement had an odds ratio of 1.597 of being unvaccinated. The pairwise interaction model had an Akaike Information Criterion (AIC; an estimator of the relative fit of the statistical model to the data) value of 6761.3, whilst the main effects model had a lower AIC of 6728.5. Thus, as well as there being only one significant pairwise interaction, the AIC of the pairwise model suggested it was not as good at explaining the variation in the data as the main effects model. Hence, the following results are from the main effects model (Table 1).  
Figure 4: Percentage of each breed type that is sterilised.  
Age was found to have the most significant effect on vaccination; as dogs got older they became more likely to be vaccinated. Puppies had an odds ratio of 10.951; in other words, puppies were over 10 times more likely to be unvaccinated than old adults of seven years or older. Juveniles were also more likely to be unvaccinated, with an odds ratio of 1.908, and as such were nearly twice as likely to be unvaccinated than old adults. Adults had similar odds of being vaccinated to old adults. Dog type was found to be significant; the odds ratio for mixed breed dogs being not vaccinated was 1.276 and for Bali dogs the odds ratio was 1.348 as compared to purebred dogs. Confinement method was found to be significant; the odds ratio of dogs allowed to roam freely not being vaccinated was 1.286 compared to those confined to a house or yard. Gender was found to be a non-significant predictor of vaccination.
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
## 5 Conclusions  
The purpose of the current study was to conduct LTD system reliability analysis with load-sharing mechanism considered. A unified load-sharing rule structure and a self-adaptive Monte Carlo simulation method are proposed when we evaluate the reliability. With numerical simulation, we have found that the more sensitive to the distance factor the load-sharing is, the more noticeable fault centrality within each level is. Besides, no matter what load-sharing rule we adopt, there is the same trend in the level reliability degradation process, that is, a steady early segment, a rapidly declining middle segment, and a later segment close to zero. But the point at which the level reliability begins to decrease obviously and the corresponding decreasing rate are greatly affected by the load-sharing rule. A load-sharing rule that is sensitive to the distance factor has a slower degradation process. Additionally, we must control the pre-specified working coefficients below a certain value  
Figure 6: Current trigger strategy.  
Figure 7: System reliability with the current trigger strategy.  
as well as selecting a suitable trigger unit in order to obtain considerable system reliability. Those findings contribute in several ways to our understanding of the load-sharing mechanism and have significant implications for the understanding of how the load-sharing mechanism influences LTD system reliability.  
In most parts of the discussion, we compare the different impacts among equal, linear, exponential, and local-equal load-sharing rules. Specially, the linear load-sharing rule is used to analyze system reliability with the current trigger strategy considered, which is empirically inferred as an appropriate rule. More experiments will need to be done to find a more appropriate type and the corresponding parameter of the load-sharing rule. Accordingly, the module failure threshold \(k\) could be a tradeoff between condition reliability and work efficiency when the load-sharing rule is determined. Notwithstanding these limitations, the present study has an important significance to guide the process of LTD system reliability analysis. We suggest that continued attention is needed to pay for the sake of evaluating and optimizing the LTD system reliability.  
Conceptualization, J.C., T.Z. and J.J.; methodology, J.C., J.J. and F.R.; formal analysis, J.C., and Z.C.; writing--original draft preparation, J.C.; writing--review and editing, J.C. and J.J.; project administration, J.J.; funding acquisition, T.Z. and J.J.  
This research received no external funding.  
The authors declare no conflict of interest.
## 1 Introduction
Recently, gas chromatography-ion mobility spectrometry (GC-IMS) has also been used for analyzing the flavors of foods, such as grains, edible mushrooms, meats, fishes, alcohol, tea, and fruits [16]. This technique combines the advantages of GC (high separation capacity) and IMS (fast response and high sensitivity), offering a rapid, accurate, simple, and intuitive analysis [17]. GC-IMS equipped with an automatic headspace injector allows for the detection of volatile compounds from the samples without any preprocessing and multiple organic reagents. However, few experimental studies have been performed to investigate the volatile profiles of soybean hydrolysates using GC-IMS or the combination of GC-IMS and GC-MS. This study attempted to investigate the effects of fermentation by _Tetragonococcus halophilus_ and _Zygosaccharomyces rouxii_ on the volatile profiles of soybean protein hydrolysates. GC-MS and GC-IMS were employed as identification methods to achieve a better examination of aroma profiles in different soybean hydrolysates. Orthogonal partial least squares analyses (OPLS-DAs) and partial least squares regression (PLSR) were used to further assess the effects of the fermentation on the aroma profiles of the obtained soybean hydrolysates and explain the sensory properties with volatile compounds. The results of this research could guide the process design for producing soybean protein hydrolysates with an improved volatile flavor compound (VFC) profile and demonstrate the capacities of GC-IMS and its combination with GC-MS for analyzing the VFCs of soybean protein hydrolysates.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### Computational Processing
#### 3.5.1 Time  
Latency refers to the length of transaction processing time [11]. The growing number of nodes and transactions may result in an increase in the mining and validating block's time. Even while public BCs can help speed up the insertion of a new block, it is still challenging to add blocks at the pace that IoT data are created [69]. The latency limitation has a substantial impact on scalability capabilities [69]. In addition to processing at local nodes, data transmission across nodes contributes to the latency. The latency of the BC network is determined by both of these delays. Forking is caused by latency caused by propagation delays. A miner who is not a broadcasting miner may broadcast their block in a network before receiving another miner's block. Miners can own several blocks as a result of the forking effect [57]. In healthcare, authenticating a block takes around 10 min, which might compromise security countermeasures during that time. Because any delay might affect exam analysis, healthcare systems must be flexible and available at all times [69]. BC transactions need time to be processed, which will lead to extra latency once applied in BIoT [75].  
Feasible solutions may include data chunking or the detection of indicators such as departures from predicted thresholds. Propagation delay must be kept to a minimum to avoid the forking effect. The closest neighbor selection (CNS) technique was utilized in [168] to decrease the propagation latency in the BC network. Another method of avoiding the forking impact is to utilize acknowledgment when a new block is received, to signal whether or not forking has happened [92]. Following the fork, the process of block generation is restarted. This procedure repeats itself until the block is updated without forking. Because there is a chance of a rollback if a lengthy chain does not include the necessary block in the future, the forking effect produces probabilistic confirmation of transactions [57].