esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing and Annotation  
The LC-HRMS/MS spectra of each sample were processed in the MZmine version 2.53 software. The parameters used in the positive ion mode ionization data processing were mass detection as 1.0 \(\times\) 10\({}^{7}\) for MS1 and 1.0 \(\times\) 10\({}^{4}\) for MS2, while the negative ion mode ionization was 2.0 \(\times\) 10\({}^{6}\) for MS1 and 1.0 \(\times\) 10\({}^{3}\) for MS2. Other parameters were used for both ionization modes: ADAP Chromatogram builder algorithm for chromatogram construction and wavelets performance deconvolution. Isotopes were removed through the Isotopic Peaks Grouper, using the most intense isotope as a representative, and aligned with the join aligner tool. The data were tentatively identified using a custom database search from the _Laboratorio de Fitoquimica e Farmacognosa_ (Laboratory of Phytochemistry and Pharmacognosy) at _Universidade Federal do Rio de Janeiro_ (UFRJ, Rio de Janeiro, RJ, Brazil). Furthermore, the unitary mass and fragmentation patterns were compared with data from PubChem and MassBank websites, and the GNPS platform. Finally, a Venn diagram was constructed using the Bioinformatics & Evolutionary Genomics webserver (bioinformatics.psb.ugent.be/webtools/Venn/) to compare the ion profiles of samples.
## 6 Evaluation of RPL and RPAL in a Real World Environment
### Evaluation Results
#### 6.2.2 Network Churns  
In Figure 7b, the statistical oscillation count of the targeted network is depicted. Since the captured DODAG structures during the test cannot all be listed here, the number of network churns is adopted to present the stability of the observed DODAG topology.  
The results prove that the RPAL model is able to reduce network oscillation compared with the original RPL model. The main reason is that standard RPL protocol adopts ETX to find high throughput paths. Since ETX is always searching for the best instantaneous link quality and the packet delivery ratio (PDR) estimated by sending probes is a stochastic variable in a real world environment, instability may be unnecessarily induced in our static network, even hysteresis has been used to reduce churn in response to small ETX metric changes. Due to the existence of weight parameters in our RPAL model, the effect of hysteresis is magnified and this decreases the count of the switching parent. In addition, node 4 is used to be a key router in the 1 st field test, and its unreliable characteristics will lead to more churns for its potential children (nodes 5, 6 and 10). Last but not least, it should be noticed that the RPAL model may also cause network churns due to its selection of a new routing path to avoid the battery depleting nodes. However, in our case, this impact is very limited and the evaluation results prove that the mechanisms in SCAOF will not produce serious oscillations in a wide range of existing DODAG.
## 3 Results and Discussions
### Fitting to Mechanical Strength  
Through nonlinear regression analysis of experimental data (as shown in Figure 7), the optimal carbon/glass fiber hybrid ratio for SEA was determined. The derived constitutive equation enables mechanical property prediction of honeycomb sandwich composites based on specific hybrid ratios. The fitting equation is expressed as follows:  
\[y=A+Bx+Cx^{2}+Dx^{3} \tag{1}\]  
where \(y\) is the SEA of sample, \(J/g\), \(x\) is the hybrid ratio, and \(A\), \(B\), \(C\) and \(D\) are constants.  
Figure 6: Impact damage morphology of the sample: (**a**) S-1 impacted surface; (**b**) S-2 impacted surface; (**c**) S-3 impacted surface; (**d**) S-4 impacted surface; (**e**) S-1 backwash surface; (**f**) S-2 backwash surface; (**g**) S-3 backwash surface; (**h**) S-4 backwash surface.  
Figure 7: Fitting of the SEA to hybrid ratio of samples and its first derivative curve: (**a**) Bending SEA fitting; (**b**) derivative of bending fitting; (**c**) impact SEA fitting; (**d**) derivative of impact fitting.  
Table 4 shows the R\({}^{2}\) values of 0.99 and 0.97 for specific energy absorption (SEA) in bending and impact processes, respectively, across specimens with different hybrid ratios. The theoretical curves demonstrate excellent agreement with experimental data, validating the constitutive equation's predictive capability for energy absorption variation across hybrid ratios. Therefore, the fitted equation could be used to predict the variation in the energy absorption of samples with the hybrid ratio variation. First derivative analysis of the fitted curves (Figure 7b,d) yields optimal mass ratios of 0.79 and 0.68 for maximum energy absorption in bending and impact processes, respectively. The hybrid ratio obtained with the impact SEA as the target value is very close to the experimental hybrid ratio, so the optimal hybrid ratio with the impact SEA as the target value is 0.67. Samples with a 0.79 hybrid ratio were experimentally validated through bidirectional (L/W) bending tests (Table 5), confirming the constitutive equation's accuracy. Nonlinear regression analysis established optimal hybrid ratios of 0.79 (bending) and 0.67 (impact) with experimental verification.
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites
### Carrier-to-Noise Density Ratio (C/N\({}_{0}\))  
The average _C/N\({}_{0}\)_ values against elevation is used here to investigate the characteristics of the BDS-3 BIC/B2a signals. For definition of the _C/N\({}_{0}\)_ and the procedures of how to compute the average _C/N\({}_{0}\)_ values against elevation for each available signal, please refer to our previous work for detail [7]. The _C/N\({}_{0}\)_ values against elevation for BDS-3, GPS, and Galileo with the iGMAS and M300 Pro receivers are plotted in Figure 3. In general, the _C/N\({}_{0}\)_ values increase with the increasing of satellite elevation angles for both receivers, from about 30 dB-Hz at low elevation angles to approximately 50 dB-Hz close to the zenith. Moreover, for the iGMAS receiver, it is obvious that the BIC signal exhibits approximately 2-8 dB-Hz lower _C/N\({}_{0}\)_ values than other signals over all the elevation angles. This is reasonable considering that the data component is tracked for BIC signal [7]. With respect to the M300 Pro receiver, however, insignificant differences are observed for the obtained _C/N\({}_{0}\)_ values between BIC and other signals, which may due to the reason that the pilot component is tracked for B1C signal with M300 Pro.
# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 3 Prediction of Additional Taxi-Out Time
### Calculation of UTT  
To predict the ATT, first we need to obtain the actual value of the ATT. Since relevant data are not available directly, they can only be calculated by subtracting the UTT from the departure taxi-out time. To calculate the UTT, we use the linear regression-based method developed by the Aviation Policy and Planning Office (APO) of the United States, in which airlines are grouped by using only those that participate in the Airline Service Quality Performance (ASQP) system, while assigning an average value to the other airlines at the airport. This analysis is more applicable to the operational performance of airlines rather than airports. Additionally, for busy multi-runway airports where the distribution of airline parking spots is not significant, this method may lead to an inaccurate calculation of the ATT. Based on this, we propose a method for obtaining the UTT that considers gate groups and runways, as shown in Figure 3. We first pair the twenty-two stand groups with the four departure runways to create eighty-eight origin-destination pairs (ODPs) and calculate the UTT for each ODP. Then, we analyze the arrival and departure flows and select the flow values most strongly correlated with the taxi-out time as explanatory variables. Finally, we establish a regression model to obtain the UTT for each stand group and runway pairing pattern when there are no other departure or arrival flights.  
Figure 3: Calculation process of UTT.
# NiO-Based Electronic Flexible Devices
## 4 Electronic Flexible Devices Based on Other Oxides
This was achieved, so far in a Ni/Sm\({}_{2}\)O\({}_{\mathrm{S}}\)/ITO device, which can operate at very low power (set: 0.25 V, 100 uA and vice versa, reset: 0.20 V, 49 uA) [129] or more recently with Ag/Au-chitosan/Au (bipolar, set voltage of 2.5 V) [130], which is biocompatible, but does not overcome the costs and fabrication related issues. The development of flexible electrochromic devices is connected to the possibility of achieving a uniform layer of color changing material on a flexible substrate, capable of retaining the properties under stress and over time. Besides NiO based materials, recent developments were achieved using 2D TiO\({}_{2}\)/MXene heterostructures on PET [131]. More in detail, TiO\({}_{2}\)/Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) heterostructures were employed where the Ti\({}_{3}\)C\({}_{2}\)T\({}_{\mathrm{x}}\) film serves as the transparent conductive electrode, and the TiO\({}_{2}\) film serves as the EC layer in EC devices. The heterostructure was prepared through an aerogel formation, dispersion in IPA and ensuing deposition on the substrate. At 550 nm, the transmittance variation reaches its maximum at around 60%, and it is largely retained upon 1000 EC cycles (92%), thus offering a practical alternative to NiO based EC devices. A different issue is the employment NiO in perovskite flexible solar cells since it is a rather standard layer for the hole transport and difficult to replace. On average, NiO-based devices have the advantage of a low cost, especially when compared to Au/Pt based thermometers, or to HfO\({}_{\mathrm{x}}\) ReRAM, as well as a better retention of properties upon deformation. In addition, NiO-based devices have more stable switching properties.
## 4 Discussion
However, they found that the CMJ propulsive impulse duration was not significant and was weakly correlated with weightlifting performance (\(p>0.004\)r = 0.22-0.28), suggesting that the net force developed during the propulsive phase of the CMJ was the primary factor between CMJ propulsion impulse and weightlifting performance. These findings align with our results, where we also found significant and nearly perfect correlations between CMJ and DSJ propulsive net impulse and weightlifting performance (\(p\) < 0.001, r = 0.96-0.98) for all subjects and also when differentiating by sex (males: \(p\) < 0.05, r = 0.93-0.96; females: \(p\) < 0.05, r = 0.76-0.87) (Table 2, Figures 4 and 5). Since weightlifters are heavily dependent on their lower body force generation capacity, the force-time metrics during the CMJ and DSJ can potentially be a better reflection of the ballistic neuromuscular capabilities of top-level weightlifters. To the best of our knowledge, this is the first study that includes a correlation analysis of front and back squat maximum strength, CMJ and DSJ force-time metrics, and weightlifting performance in top-level weightlifters. In our study, prediction equations from the regression analysis are not shown since the biggest limitation of this study is the inclusion of a small and heterogenous sample size (\(n\) = 13; 6 males and 7 females) of selected top-level weightlifters, which may be difficult to extrapolate to weightlifters of other levels (i.e., national and amateur). Additionally, because weightlifters were in different weight categories and weightlifting performance is ultimately related to weight category, predicting the snatch and clean and jerk performance could have been an important flaw. Furthermore, [PERSON] et al. [12] used a stepwise multiple regression analysis to predict weightlifting performance in top-level female weightlifters and found that the isometric mid-high pull peak force and CMJ peak power accounted for 91-95% of the variance for the snatch, clean and jerk, and weightlifting total. However, [PERSON] et al. [12] employed a longitudinal analysis to test the feasibility of the cross-sectional evaluations in female weightlifters and reflect how the changes in their neuromuscular capabilities affect weightlifting performance over the season. Future research with top-level weightlifters should address this issue with a similar approach to offer guidance to the strength and conditioning professionals working with male and female top-level weightlifters [12; 17; 18]. Individual confounding factors such as the subjects' diet, sleep, or recovery may have influenced the results of this study. These results were taken during the tapering prior to the IWF World Weightlifting Championships and may not represent other phases of a given training cycle.
# Design and Dynamic Simulation Verification of an On-Orbit Operation-Based Modular Space Robot
## 2 Configuration Design of Reconfigurable Modular Space Robot  
### Space Mission Description  
Reconfigurable robots can be used in fields such as on-orbit service and planet exploration, as they can change structures according to mission type or working environments. The existing space robots mainly adopt traditional configurations of joint linkage modes, which cannot achieve a self-reconstruction function. Thus, structural change is the basic function of space modular reconfigurable robots. Due to the modular docking and transformation solution, modular space robots can achieve system upgrades, maintenance, expansion, and other missions that are difficult to achieve with traditional space manipulators through the addition and removal of modules. To deal with this problem, this work firstly describes the space mission parameters for the addition of modules, as well as for removal and shape reconstruction, based on which the design principles of robot modules are determined.
###### Abstract  
The improved peri-implant bone response demonstrated when utilizing the platform-switching concept may result from the reduced levels of metal ions released from implant-abutment surfaces to the surrounding tissues. These corrosion products may play a major role in crestal bone remodeling around dental implants. This study evaluated the effect of different implant-abutment couplings (platform-matched vs. platform-switched) on osteoblasts' function. Titanium alloy and cobalt-chrome alloy abutments were coupled with titanium cylinders, forming either platform-switched or platform-matched groups, and were incubated in human osteoblast cultures utilizing a novel direct-exposure technique. Viability was evaluated over 21 days using Alamar Blue assay. Apoptosis was measured after 24 h using flow cytometry. The expression of genes related to bone resorption was analysed over 21 days using a real-time quantitative polymerase chain reaction assay. Cell viability was reduced from day 4 to day 21 (_p_ < 0.05), with higher rates of early apoptosis (_p_ < 0.05) compared to the controls. Apoptosis was higher in the platform-matched groups (_p_ < 0.05). The tested genes' expression was up-regulated after 1 and 3 days of exposure to implant-abutment couplings (_p_ < 0.05). The upregulation was more pronounced in platform-matched groups (_p_ < 0.05). Exposure of osteoblasts to implant-abutment couplings induced adverse biological responses, which were more pronounced with platform-matched couplings. These reactions might be related to the increased amounts of metal ions released from the platform-matched couplings, highlighting the possible role of corrosion products in the mediation of crestal bone loss around dental implants.  
surface corrosion; dental implants; peri-implantitis; alveolar bone loss; platform-switching; titanium; metal ions; osteoblasts
# Production of Sustainable Postbiotics from Sugarcane Straw for Potential Food Applications
## 2 Materials and Methods
### Production of Postbiotic Extract  
#### 2.2.1. _S_ugarcane Straw Saccharification Conditions  
Raizen (Sao Paulo, Brazil) contributed the sugarcane straw. Samples were transported to the CBQF-UCP laboratory (Porto, Portugal) and immediately dried in a furnace (Nabertherm, Lilienthal, Germany) at 40 \({}^{\circ}\)C and crushed (knife mill SM100, Retsch, Haan, Germany) to a particle size \(<\) 4 mm. Subsequently, it was sieved on a 900 \(\upmu\)m sieve (Retsch GmbH, Haan, Germany), with an amplitude of 100 for 10 min.  
Straw powder was dispersed in a supplemented buffer solution in a ratio of 1:20 (\(v\):\(w\)). Supplement solution was composed of 10 g/L peptone, 5 g/L yeast extract, 2 g/L tri-ammonium citrate (C\({}_{6}\)H\({}_{14}\)N\({}_{2}\)O\({}_{7}\)), 2 g/L potassium phosphate dibasic (K\({}_{2}\)HPO\({}_{4}\)), 5 g/L sodium acetate (NaO\({}_{2}\)CCH\({}_{3}\)), 0.1 g/L magnesium sulfate heptahydrate (MgSO\({}_{4}\):7H\({}_{2}\)O), and 0.05 g/L manganese (II) sulfate tetrahydrate (MNSO\({}_{4}\):4H\({}_{2}\)O). This supplement was added to the sodium citrate buffer solution, containing 4.31 g/L of citric acid (C\({}_{6}\)H\({}_{8}\)O\({}_{7}\)) and 8.68 g/L sodium citrate (NA\({}_{3}\)C\({}_{6}\)H\({}_{5}\)O\({}_{7}\)), and then the pH was adjusted to 5.0 with NaOH (1 M) and autoclaved at 121 \({}^{\circ}\)C for 10 min.  
The saccharification process was performed for 72 h with 36.2 FPU (filter paper units) of Celluclast(r) per gram cellulose at 50 \({}^{\circ}\)C, 150 rpm [21]. A saccharification control was performed with a sample under the same conditions without enzyme addition. All the experimental conditions were performed in duplicate. Along the process, samples were collected after 0, 24, 48, and 72 h, and the total sugars decay was determined along the saccharification process.  
At the end of saccharification, the biomass was separated by centrifugation (15 min at 5000 rpm) and then filtered (0.22 \(\upmu\)m, Sartorius stedim, Gottingen, Germany) under sterile conditions.
## 3 Results
### Baseline and Result  
The evaluation work is to randomly select 90% of the dataset as the training set, and the remaining 10% as the test set. Test data will not be used during model training. In order to better verify the effect of the method we designed, we selected the current mainstream image classification method and some pre-trained language models as a comparison. Graph classification methods include SortPool, DiffPool, gPool, SAGPool, and our improved source Graph U-Nets. The direct text classification results of the pre-trained language model are also included in the indicators, including BERT-base (Chinese), BERT-large (Chinese), and BERT-wwm-ext (Chinese), three Chinese pre-trained language models. The evaluation index is the accuracy of classification. During the test, all the data marked by the three testers will be tested independently, and whether the ability to process long textscan be included in the assessment indicators. In terms of hyperparameter settings for the baseline, we choose to refer to the SAGPool. The experimental results are shown in Table 1. The method we designed is better than the graph classification methods, but not better than the pre-trained language models.  
However, we all know that it is unfair to let the graph classification method directly compare the classification results with the pre-trained language model because it involves the loss of information from the text to the graph. Moreover, the pre-trained language model is obtained after a lot of computing power, time and data training, and as the length of the text increases, the complexity of training the pre-trained language model is a square level increase, so most pre-trained languages models set the maximum length of the processed text to 512. In the experiment, directly using the pre-trained language model for news text classification is to discard the text that cannot be processed, and directly process the news text with the text length less than 512. Therefore, it can be concluded that the model we have designed has strong capabilities in both accuracy and processing long-text news.  
According to the requirements of our scientific research projects, in addition to classifying news trend forecasts, we also need to display them visually, so we use knowledge graphs to visualize them. Regarding the question of why not directly use the pre-trained language model, but also use the pre-trained language model to obtain the initial embedding vector, we just used the pre-trained language model to obtain the initial vector. In fact, using any other method to obtain the word embedding vector, such as the gensim Chinese word embedding vector method, we can also obtain the initial embedding vector of the node, but it does this. The effect is a little worse than using BERT. We can also see that from Table 1 Attention Graph U-Nets (gensim) and Attention Graph U-Nets (BERT).
## 3 Experiments, Comparative Investigations and Results
Thus, the AECR of E11 attains a minimal value (1.77 J). At the same time, E10 secures 1.88 J which is better than other works such as E9 and E12. Similarly, Figure 16 shows the calculation of the average routing delay in milliseconds. Figure 15: Energy-efficient routing schemes and AECR. The routing delay of E10 and E11 are minimal compared to E9 and E12. The category of E9 and E12 fall under clustered or zonal network architecture. Under these network management policies, the route construction to deliver the data from source to destination has to follow clustered or zonal rules. This makes the routing delay a little higher than flat network architectures (E10 and E11). In this regard, E11 strongly builds stable links and coverage assurance in the WSN for data transmission. Once this process has been successfully developed, it reduces the routing delay during data transmission. Thus, it minimizes the delay (495 ms). In the same manner, E10 produces 510 ms of routing delay, which still better than E9 and E12. Figure 17 illustrates the computation overhead of E9, E10, E11 and E12. As discussed, E9 and E12 produce more computation overhead (additional packet transmission) than E10 and E11. The observed results of E9 (145) and E12 (170) are closely related to AACR produced by each technique. In contrast, E10 and E11 managed the computation overhead with minimal rates compared to E9 and E12. The reason for the optimized overhead of E10 and E11 is the energy stability and link stability in the network. In this manner, E10 produces 140 additional transmissions and E12 produces 110 additional transmissions in order to stabilize network communications. The observations are gathered against the number of actively participating sensor nodes for each iteration. Finally, Figure 18 illustrates the average packet delivery ratio (PDR) attained by each work. In this experiment, E10 and E11 obtained 0.93 to 0.94 of average PDR for a maximally populated network (300 sensor nodes). On the other hand, E9 and E12 sent packets at the PDR of 0.9. In general, the difference in PDR produced by each system is not crucial, yet the delay and energy consumptions significantly vary due to network changes. Hence, the comprehensive experimental analysis and technical discussion described in this article clarify the crucial efforts of energy-efficient WMAC policies and routing strategies to extend the lifetime of sensor nodes. Figure 16: Energy-efficient routing schemes and Delay. Figure 17: Energy-efficient routing schemes and Overhead. Figure 18: Energy-efficient routing schemes and Average PDR.