esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of Ultrasound Amplitude on Bioactive Extraction in Different Solvents  
The results showing the influence of the amplitude on the extraction of bioactive compounds from BC in water and ethanol are collected in Table 1. Water and ethanol are frequently used for antioxidant extraction as they show high efficiency, moderate polarity and low toxicity, which make them appropriate for food industry applications [17]. The hydroalcoholic mixture changes the solvent polarity, which can affect the solubility of the phenolic constituents [17]. Even though water has reduced selectivity compared to ethanol and requires more energy for solvent evaporation, it offers the advantage that, when used in combination with ethanol, it provides a sustainable approach for the industrial vaporization of bioresources rich in antioxidants.  
In water extracts, phenolic recovery was not affected by the ultrasound amplitude, whereas, in ethanolic extracts, only the TPC was affected by amplitude variations, with the highest content being reported at a 30% amplitude (86.16 \(\pm\) 0.66 mg GAE/g d.w.). Abovethis limit, the TPC decreased by about 15% (\(p<0.05\)). On the other hand, when compared to conventional extraction, regardless of the amplitude of sonication, in most cases, UAE was able to recover similar or even higher concentrations of phenolics, indicating its potential in minimizing the extraction costs. For example, UAE was able to recover similar TPC after only 10 min when using water (Table 1), while, in ethanolic extracts, sonication at 30% for 10 min produced higher TPC than conventional extraction for 24 h (Table 1). Although the DPPH scavenging activity (Table 1) reported in water extracts after 24 h of conventional extraction was about 20% higher than that obtained during UAE at 30% for 10 min, the results highlight the excellent potential of ultrasound treatment in producing sustainable extracts. Larger differences were obtained for the ABTS\({}^{+}\) scavenging activity, where UAE (regardless of the amplitude value) recovered about 40% (water samples) and 200% (ethanol samples) more antioxidants compared with conventional extraction. Regardless of the extraction method, the use of a mixture of ethanol and water as an extraction solvent contributed to the higher recovery of all bioactive compounds from BC pomace, when compared to using only water. Similar results have been reported by [PERSON] et al. [17], who tested the effects of the ethanol concentration on the extraction efficiency of phenolic antioxidants from BC. The authors reported about two-fold higher yields of extraction when using 50% ethanol compared to water. The results of our study in both water and ethanol are higher than those reported by [PERSON] et al. [32]. By using UAE at 100 W power to recover the phenolic constituents from BC pomace using methanol combined with trifluoroacetic acid, the authors reported a TPC value of 16.41 \(\pm\) 0.04 mg/g d.w. Based on the results presented above, we selected an amplitude of 30% to further investigate the effects of the ultrasound exposure time on BC phenolic recovery.
## 3 Results
### Population Structure and Genome-Wide Association Analysis  
All of the pairwise genetic distances among the 104 rapeseed lines were determined from the SNP genotypes, and 3476 independent SNPs were used for calculating population structure. Population structure was assessed for K values ranging from 1 to 10 with the lowest cross-validation error using the ADMIXTURE software. As a result, the LD decay changed continuously, and when K = 5, the cross-validation error was the lowest (Figure S3). For STRUCTURE, variation of Delta K suggested that the population could be assigned to seven groups (Figure S3C). Furthermore, a neighbor-joining tree determined from the SNP genotypes showed that the 104 rapeseed lines could be classified into five divergent groups (Figure S3D). Therefore, PCA = 5 was applied as the covariance for GWAS.  
To ensure the reliability of GWAS results, genome-wide association mapping was executed with six algorithms by Tassel5 [16] and mrMLM [20]. Most of the ideal models were presented in accordance with the observed \(-\)log10(\(p\)-value) vs. the expected \(-\)log10(\(p\)-value) in the QQ plots (Figures S4 and S5). For different algorithms (i.e., SNPs detected by the same trait in different environments), there were differences in the detections of QTL of the six methods (Figure 2A), FASTmrEMMA (70), FASTmrMLM (318), ISIS EM-BLASSO (236), mrMLM (188), pLARmEB (300), and MLM (163). Among them, 183 SNPs detected by at least two algorithms were considered as credible loci. For different environments (i.e., SNPs detected by the same trait with different algorithms), a total of 279, 260, 305, and 431 loci were detected in 2018, 2019, 2021, and BLUP, respectively (Figure S2B). One hundred and seventeen significant SNPs (the same SNPs for different traits were treated as the repeated SNPs) associated with a certain phenotype were identified by any two environments of 2018, 2019, 2021, and BLUP, which were considered as credible loci as well. The SNPs C06_22559430 and A03_22103527 were simultaneously detected by the four environments of NSS and TSW, respectively (Table S2). Nine significant SNPs were identified across three different environments related to PH (4/9), SL (3/9), NSS (1/9), and MINS (1/9). These SNPs repeatedly detected in multiple environments can be regarded as reliable loci for controlling traits.  
We combined the above credible SNPs detected by different algorithms and environments and excluded the minor QTLs with PVE of less than 1%. Three hundred and twenty-four QTLs involved in 212 SNPs above were used for further analysis. The traits mostly detected at QTLs were BAI (39/324), MISM (37/324), NSS (35/324), SL (34/324), TSW (34/324), PH (33/324), and MIL (26/324) (Table S3). These QTL were widely distributed on 19 chromosomes. The most distributed chromosomes were A01, A03, A05, C05, C06, and C09. Chromosome A08 contained only one SNP associated with MISM at A08_16931989. Five SNPs for the harvest index were detected on A05, C05, C08, and C09, three for BYP on A06, C02, and C05, and fourteen for SYP, mainly on A01, C04, A03_random, and A06_random.
# Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules
## 2 ACO-OFDM System Model
### ACO-OFDM Signal Transmission over FSO Turbulent Channel
To accomplish this, DL is proposed as a method that can replace the regular ACO-OFDM detectors to overcome the above problems and to enable the use of ACO-OFDM over different FSO turbulence channels, as explained in the next section.
# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
###### Abstract  
Land surface temperature (LST) is a vital parameter associated with the land-atmosphere interface. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product can provide precise LST with high time resolution, and is widely applied in various remote sensing temperature research. However, due to its inability to penetrate the cloud and fog, its quality is not able to meet the requirements of actual research. Hence, obtaining continuous and cloudless MODIS LST datasets remains challenging for researchers. The critical point is to reconstruct missing pixels. To compare the performance of different methods, first, three kinds of methods were used to reconstruct the missing pixels, namely, temporal, spatial, and spatiotemporal methods. The predicted values using these methods were validated by the automatic weather system data (AWS) in the Heihe river basin of China. The results demonstrated that, compared with other methods, linear temporal interpolation using Aqua data had the best performance in MODIS LST reconstruction in the Heihe river basin, with an RMSE of 7.13 K and an R\({}^{2}\) of 0.82, and the NSE and PBias were 0.78 and \(-\)0.76%, respectively. Furthermore, the interpolation method was improved using adaptive windows and robust regression. First, the international Geosphere-Biosphere Program (IGBP) classification was employed to distinguish the different land surface types. Then, the invalid LST values were reconstructed using adjacent days' effective LST values combined with a robust regression. Finally, a mean filter was applied to eliminate outliers. The overall results combined with ERA5 data were validated by AWS, with an RMSE of 6.96 K and an R\({}^{2}\) of 0.79 and the NSE and PBias were 0.77 and \(-\)0.20%, respectively. The validation demonstrated that the scheme proposed in this paper is able to accurately reconstruct the missing values and improve the accuracy of the interpolation method to a certain extent when reconstructing MODIS LST.  
land surface temperature (LST); remote sensing; interpolation; reconstruction; MODIS +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Input Datasets for ANNs  
After performing the high-temperature storage test, degraded specimens were obtained as shown in Figure 7. The color of the specimens was changed by the accelerated life test. The FTIR measurement was performed on the prescribed position. Figure 8 shows five FTIR measurements per split. In this figure, dABS and dPLA denote the degraded ABS specimen and the degrade PLA specimen. Other researchers have reported characteristic bands for thermal effects. In the case of ABS, the thermal degradation makes the absorption peaks for alkenes and aromatic compounds increase [9]. The characteristic bands of alkenes and aromatic compounds are 3074 cm\({}^{-1}\), 1630 cm\({}^{-1}\), 910 cm\({}^{-1}\), 3033 cm\({}^{-1}\), 1496 cm\({}^{-1}\), and 698 cm\({}^{-1}\). [PERSON] et al. [15] reported that the characteristic bands for PLA are related to crystalline structure, which are 1207 cm\({}^{-1}\), and 920 cm\({}^{-1}\). However, the IR spectra for PLA and dPLA are hard to be distinguished from each other using classical interpretation methods. When the IR spectra are measured, noise factors involved in the spectra are inevitable, which are typically caused by measures, the environment, and so on. Noise factors can lead to misinterpretation  
Figure 6: Activation functions.  
of spectra. The methodology using ANNs might help to correctly interpret the IR spectra involving various noise factors.
# Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
## 5 Conclusions  
The aim of this paper was to propose a hidden Markov model-based dynamic hard shoulder opening strategy in a hybrid network environment to reduce the impact of the dynamic penetration rate \(\rho_{CAV}\). The following three main works were carried out. First, a traffic breakdown prediction model based on HMM in a hybrid network environment was proposed to extend the scope of application of the traditional traffic breakdown prediction model. Then, the characteristics of traffic breakdown in a hybrid network environment were explored to provide a basis for dynamic hard shoulder opening. Finally, the traffic breakdown events predicted by HMMD were used as a discriminative condition for the opening of the hard shoulder, which further extends the hard shoulder opening application scenarios and improves the accuracy of hard shoulder opening conditions in hybrid network environments. The simulation results showed the following. (1) The HMM model is effective in predicting traffic breakdown in mixed network environments. (2) In a mixed network environment, the probability of traffic breakdown increases with \(\rho\), as the evaluation criteria for crashes caused by an increase in the overall average speed become more stringent. (3) Compared to LMD-HSR and HMM-HSR, the use of HMMD-HSR in hybrid network environments can effectively reduce the occurrence of traffic crashes. Additionally, the optimization effect of HMMD-HSR improves with higher \(\rho\) values.  
Research on congestion in mixed network environments is limited, and this paper did not comprehensively analyze the factors affecting traffic breakdown in mixed network environments. Additionally, it is noteworthy that a higher \(\rho\) increases the likelihood of traffic crashes. In the future, we will explore the causes of traffic breakdown in mixed network environments more comprehensively to better address their occurrence.  
Conceptualization, J.Y.; Writing--original draft preparation, Y.Q.; Methodology, Z.F.; Supervision, review and editing, J.Z.; Review and editing, H.Z.; Review and editing, T.C.; Review and editing, S.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key R&D Program of China (Grant 2021 YFB 1600500), the Zhejiang Provincial Department of Transportation, Jiangsu Provincial Transportation Technology and Achievement Transformation Project (No. 2023Y06), and the Natural Science Foundation of Tibet Autonomous Region (XZ20201 ZR0040G).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Zhejiang Communications Investment Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 The Node Deployment Strategy Based on 2-Fold Coverage
### Node Deployment  
From the three coverage-based QoS metrics proposed above, a node deployment strategy needs to improve information integrity and validity, as well as reduce information redundancy as much as possible.  
According to existing research, when conducting a 1-fold coverage-based node deployment, if the full coverage condition is satisfied and the number of nodes in the area is minimized, it is necessary to make full use of the coverage disk of each node; that is, the overlapping area between the nodes should be as small as possible. This study found that the effect of constructing a hexagonal honeycomb grid is optimal. Based on this, we propose a 2-fold coverage-based node deployment strategy in a cellular network for the case where the coverage requirement of all monitoring target nodes in the region is 2, as shown in Figure 3.  
As shown in Figure 4, the node deployment strategy in this paper is based on a cellular network. The main steps are as follows:  
1. A cellular network is built in the monitoring area.
2. The sensor nodes are arranged at the upper left, upper right, and lower middle vertices of the grid. At this point, the 1-fold coverage-based node deployment is completed.
3. The sensor nodes are arranged at the vertices of the center of the grid. At this point, the 2-covered node is deployed.
## 3 Integrated Disaster Resource Scheduling Modeling
### Pre-Disaster Resource and Facility Optimization
#### 3.1.1 Pre-Disaster Resource Optimization  
Based on the optimization framework, we define the cost-minimization objective function as follows:  
\[\min_{x,d,r}(c_{w}(x_{w})+c_{e}(x_{e})+c_{d}(d_{w},d_{e})+c_{v}(r_{w},r_{e})) \tag{4}\]  
where \(x_{w}\) represents the total amount of freshwater resources, \(c_{w}(x_{w})\) is the total cost of purchasing freshwater resources, \(x_{e}\) is the total amount of electricity resources, \(c_{e}(x_{e})\) represents the total cost of purchasing electricity resources \(d_{w}\) and \(d_{e}\) respectively denote the allocated amounts of freshwater and electricity resources, \(c_{d}(d_{w},d_{e})\) denotes the allocation cost associated with the unit allocation price multiplied by the corresponding allocated quantity, \(r_{w}\) and \(r_{e}\) represent the number of deployed freshwater and energy storage vehicles, and \(c_{v}(r_{w},r_{e})\) denotes the deployment cost, including fixed costs such as vehicle purchase, operation, and maintenance.
## 3 Results
### Subsection Importance Analysis of Influencing Factors
Figure 9: Scatter plot between each environmental factor and the CO\({}_{2}\) concentration: (**a**) between temperature and CO\({}_{2}\); (**b**) between Humidity and CO\({}_{2}\); (**c**) between Illuminance and CO\({}_{2}\); (**d**) between O\({}_{2}\) and CO\({}_{2}\); (**e**) between NH\({}_{3}\) and CO\({}_{2}\); (**f**) between PM\({}_{2.5}\) and CO\({}_{2}\); (**g**) between PM\({}_{10}\) and CO\({}_{2}\); (**h**) between Indoor population and CO\({}_{2}\); (**i**) between CO\({}_{2\_\text{pre}}\) and CO\({}_{2}\). fit improved to a certain extent compared to that in the previous two scenarios. However, expanding the number of input variables in the selected model also led to longer training times and increased model complexity [44]. Considering the _RMSE_, _MAE_, and _MAPE_, the results for the top seven features (excluding the NH\({}_{3}\) and O\({}_{2}\) concentrations) were indeed better than those for the top eight features (excluding the NH\({}_{3}\) concentration). However, we noted that introducing the eighth feature slightly improved the \(R^{2}\) value of the model to more than 93%. Although the difference was not significant, it indicated that the eighth feature had a positive contribution to the model, enhancing its explanatory power and stability. While the differences between the 'top seven features' and 'top eight features' and between the 'top eight features' and 'all features' are relatively small, considering the overall performance balance, moderate complexity, model stability, and feature importance, choosing the 'top eight features' as the best result is reasonable and advantageous. This finding verifies that the set of influencing factors (excluding the NH\({}_{3}\) concentration) obtained by screening the initial environmental factors in this study is effective for the LSTM model. This approach provides experimental support when selecting input variables for the subsequent prediction of CO\({}_{2}\) concentrations in classrooms.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 2 Related Works  
In OppNets, the network scale and topology information cannot be known in advance. Packets are transmitted determined by the opportunity of encounter between nodes in OppNets. Currently, opportunistic networks are mainly applied to specific fields such as wildlife tracking [6], disaster rescue [7], and vehicle-mounted monitoring networks [8]. The maritime opportunistic network introduces the opportunistic network into the marine environment, and it has both the characteristics of the opportunistic network and the marine environment [9]. In the maritime opportunistic network, vessels are regarded as mobile networking nodes, and packet transmission depends on the opportunistic contacts between vessels.  
Figure 1: Illustration of routing in opportunistic networks.  
An appropriate routing scheme for opportunistic networks can provide reliable message transmissions. In recent years, there are many routing schemes for OppNets in the literature, which are summarized as follows.
## 4 Discussion
### Challenges and Limitations  
We acknowledge that our simulation, while comprehensive, is still a simplification of reality. Factors like unpredictable human behavior, multi-country regulatory environments, and integration with legacy systems are not captured. First and foremost, supply chain oversight is a process that requires some degree of mutual trust and monitoring among and between participants. During periods of major disruption, human confidence in supply chains will inevitably be degraded; our simulation cannot capture all such scenarios. Thus, the WEave methodology is really intended for routine cases and presumed to integrate into a social context that builds and reinforces trust in the techniques used.  
Even focusing on the simulation when modeling ideal deployments in an undisrupted environment leaves some issues unaddressed. Performance in a controlled environment may differ from a cloud or multi-datacenter setup. Also, Hyperledger Fabric, while powerful, is just one platform--different blockchain platforms (e.g., Ethereum-based, or newer distributed ledger technologies) might offer other advantages or face different issues (like public transparency vs. privacy trade-offs). We chose Fabric for its permissioned and private nature fitting BDCCs.  
And finally, there are inevitably questions about adoption and the life cycle of WEave blockchains themselves. Any technology is only as strong as the weakest link. A technology might work in the lab, yet if some of the human participants are devious and intent on misuse of the solution, other, more trusting individuals may feel an erosion of confidence and ultimately reject it. Moreover, the evolution of mutual trust over time is a dynamic factor that delves into change management--training people at, say, a recycling plant to use a new system, or convincing a new supplier to input data diligently. These \"soft\" challenges often determine success more than the technology itself. In the paper, we can only point this out; addressing it may need pilot programs and stakeholder workshops. Here are more technical challenges:
###### Abstract
The existed studies indicated that the quality of precise products could affect the multi-GNSS PPP performance [25]. However, few studies related to the consistency of precise products among different analysis centers for BDS-3 satellites. In addition, the corresponding conclusions for GPS, GLONASS, Galileo, BDS-2 and QZSS satellites in the existed studies (using the datasets before 2020) [23; 24] may be obsolete, as the quality of precise satellite products with enhanced orbit and clock determination models for these satellites has been continuously improved in recent years. According to the above description, it is necessary to assess the latest performance of five-system integrated PPP with almost fully deployed BDS-3, BDS-2, GPS, GLONASS, Galileo and QZSS (i.e., GREC-PPP). In this study, the performance of GRECJ-PPP under the current constellation status using daily observations in both static and kinematic modes in terms of position accuracy and convergence time is rigorously evaluated. In addition, the consistency of the precise products from ESA, GFZ and WHU that are able to support the five satellite systems and the effects of them on the data processing of GRECJ-PPP are also investigated. For completeness, the real-time position solutions derived from four-system integrated PPP with BDS (BDS-3/BDS-2), GPS, GLONASS and Galileo (i.e., GREC-PPP) using the real-time stream from Centre National d'Etudes Spatiales (CNES) are also provided (noted that QZSS satellites cannot be supported by CNES real-time products). For comparison, the results of single-system PPP and dual-system combined PPP are also presented. The paper starts with the GRECJ-PPP model. Subsequently, the results are presented, including the description of precise products for the five satellite systems, the availability of five-system combination, the consistency of precise products from different analysis centers, and the performance of multi-system combined PPP. Then, the discussion is conducted. Finally, we summarize the main points and the conclusions.