esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial and Temporal Variation of GPP and Its Response to Urban Environmental Changes in Beijing
## 4 Discussion
### Influence of LUCC on AGPP in Beijing  
Land Use and Land Cover Change (LUCC) can exert an obvious influence on AGPP. For example, the conversion of vegetated areas into impervious surfaces can lead to a reduction in AGPP. Consequently, we also examined the effects of LUCC on AGPP using the CNLUCC dataset. To facilitate analysis, we have consolidated the original CNLUCC classification system into five primary categories: Cropland, Greenland, Water area, Residential land (improvious land), and Unused land. We have compiled land use/land cover data for Beijing spanning the years 2000 to 2020. The result is shown in Figure 4.  
As depicted in Figure 4, it is evident that there has been a significant conversion of various kinds of land into impervious land within the central region of Beijing, whereas transformations among other kinds of land cover are relatively minor. Building on this observation, we conducted an analysis of land use alterations in Beijing over the period  
Figure 4: Land Use/Land Cover of Beijing in 2000 and 2020. (**a**) shows the Land Use/Land Cover of Beijing in 2000, and (**b**) shows the Land Use/Land Cover of Beijing in 2020.  
from 2000 to 2020 and compared the result with the trend of AGPP during the same period. The comparative outcomes are illustrated in Figure 5.  
The result shows that areas with a negative trend in AGPP are predominantly found where the surface cover types have been converted to impervious surfaces, while AGPP remains stable or increases in other regions. The result indicates that urban sprawl exerts an obvious negative influence on AGPP. Therefore, for cities undergoing rapid expansion, the proper planning of urban growth areas and urban growth strategies is vital for the city's green transformation and sustainable development.
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 4 Discussion
### Attention and Distraction
Additionally, previous research has demonstrated that acoustic signals, when combined with visual signs, enhance drivers' readiness to respond to hazards and critical events [31]. However, it should be noted that drivers perceive an excessive number of warnings as potentially distracting [31]. Connected vehicle features, such as in-vehicle warnings transmitted via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies, enhance drivers' situation awareness and support safer driving decisions [31]. Modern IVIS systems contain a wealth of information. However, not all information can be directly conveyed to the user, as this could lead to information overload for the user. The effectiveness of the data shared with the user depends on intuitive user interfaces [37]. In summary, both field studies indicate that attention to in-vehicle information is high, while distraction remains low regardless of gender, age, and driving experience. This is supported by eye-tracking assessments, driver interviews, and results from online questionnaires. The literature confirms this observation but also highlights additional constraints to minimize distraction, such as limiting the amount of information, ensuring clarity, and incorporating acoustic or voice-based information where possible.
# Unlocking Wellness: Probiotics as Key Drivers in Functional Food Innovation and Health Promotion
## 4 Application of Probiotics in Functional Foods
### Dairy Products
#### 4.1.2. _Probiotic Fermented Yogurt_  
Common microbial species found in probiotic-fermented yogurt include _L. bulgaricus_, _S. thermophilus_, and other probiotics, such as _P. sapphimus_[86] and _B. bifidum_[87]. Probiotics provide additional health benefits while improving yogurt's flavor and consistency and regulating intestinal flora. Evidence suggests that increased intake of probiotic-fermented yogurt may protect individuals from bone loss. For instance, an Irish study involving over 4000 individuals aged 60 years and older found that higher yogurt intake was associated with a lower risk of osteoporosis [88]. Additionally, probiotics are believed to reduce inflammation and enhance insulin effectiveness. Daily consumption of probiotic yogurt may reduce the incidence of cardiovascular disease and T2 DM [89]. For example, [PERSON] et al. [90] found that daily yogurt intake reduced the risk of T2 DM by 18%. Studies have shown that different probiotic strains exhibit unique metabolic behaviors, such as lactic acid production and amino acid synthesis, in specific environments [91], thereby affecting the nutritional composition and sensory quality of yogurt. Researchers have recently combined probiotics with beneficial ingredients, such as sea buckthorn [92] and ginger juice [93], to develop novel probiotic yogurt products with antioxidant, anti-inflammatory, and intestinal health benefits. For example, [PERSON] et al. [94] used a commercial yogurt fermenter and _L. casei_ ATCC 393 as an auxiliary fermenter, adding sea buckthorn berries as an immobilization carrier to prepare frozen yogurt. The results showed that frozen yogurt containing immobilized probiotics maintained a high survival rate during 90 days of frozen storage and exhibited excellent survival, superior organoleptic properties, and high commercial potential under gastrointestinal conditions.
## 4 Experiments
### Numeric Association Rules Mining  
#### 4.2.1 Feature Selection and Task Segmentation  
In the SWaT dataset, Pearson correlation coefficients between all features were computed using the method described in Section 3.1.1. The results are shown in Figure 6. Certain features exhibit a high degree of correlation; for instance, the Pearson correlation coefficient between the flow sensor FIT101 and the valve MV101 is as high as 97.12%. This strong dependency indicates that the valve MV101 regulates the inflow to the raw water tank, which is consistent with the interdependencies between sensors and actuators in industrial control systems environments.  
Figure 5: The figure illustrates the architecture of SWaT, which is designed to simulate a real-world water treatment plant. The testbed consists of six critical stages of water treatment, each with multiple sensors and actuators [39].  
Figure 6: Correlation relationships between features.  
To reduce the search space for swarm intelligence algorithms in mining invariant rules for ICSs and enhance search efficiency, the mining task was decomposed into smaller tasks based on feature correlations. To determine an appropriate threshold for this decomposition, we conducted an experiment where the Pearson correlation coefficient threshold, denoted by \(\theta\), was varied from 0 to 0.9 in increments of 0.1. The evaluation metrics used in this experiment were the number of rules mined and the time cost of the mining process.  
The experimental results, illustrated in Figure 7, show that when \(\theta=0.3\), the number of rules mined is maximized while the time cost remains relatively low. This suggests that setting \(\theta=0.3\) strikes a balance between the comprehensiveness of the rule set and computational efficiency. Therefore, for each feature, its correlation with other features was calculated, and only those with a correlation coefficient greater than \(\theta=0.3\) with the target feature were retained. This approach enabled the large-scale mining task to be broken down into multiple smaller tasks, each focusing on different feature combinations, with each sub-task targeting a group of highly correlated features.  
By adopting this strategy, we not only reduced the complexity of the problem but also ensured that the rules mined are more likely to capture meaningful and relevant patterns within the data, thereby improving the overall performance and interpretability of the model.
## 2 Materials and Methods
### ICP-OES and ICP-MS Analyses  
During leaching experiments, recovered solutions were analyzed using an inductively coupled plasma optical emission spectrometer (ICP-OES, model 5110 VDV, Agilent Technologies, Paris, France). As for cesium, because of the low emission intensity detected by ICP-OES, the Cs contents in the recovered solutions were determined by means of an ICP-MS spectrometer (Agilent Technologies model 7850, Paris, France).
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 2 Related Work
### Federated Learning for Fault Diagnosis  
To address concerns related to data dispersion and privacy protection, federated learning has gained attention from scholars as a promising approach for fault diagnosis in recent years. For instance, [PERSON] et al. [31] introduced a federated adversarial domain generalization network for mechanical fault diagnosis. This model achieves collaborative training between a central server and multiple clients, establishing a global fault-diagnosis model while ensuring data privacy. [PERSON] et al. [32] proposed a multi-source domain adaptive method that combines transfer learning and federated learning. By leveraging user data while preserving data privacy, this approach achieves accurate identification performance. [PERSON] et al. [33] presented a federated transfer learning framework based on a weighted joint average of differences. This framework effectively coordinates well-trained global diagnostic models with privacy protection. Additionally, they developed a dynamic weighted average algorithm using the maximum mean difference (MMD) and integrated the updated local model using an automatically learned weight. [PERSON] et al. [34] proposed the FA-FedAVG algorithm, which optimizes the model weighting strategy to enhance the influence of high-quality local models. They also introduced a model aggregation strategy based on poor accuracy to reduce iteration count and accelerate convergence during training. Moreover, [PERSON] et al. [35] proposed a joint training method that incorporates meta-learning into a federated learning framework, aiming to enhance the performance of the global meta-learner.  
Currently, the fault diagnosis with federated learning is typically accomplished through a combination of local training and cloud parameter aggregation. Building uponthis foundation, certain methods mainly concentrate on assessing the distinct contributions of individual participants to the global model. Nevertheless, opportunities for optimization persist, offering the potential to investigate the allocation of varying weights to participants. This exploration could enhance the precision and personalization of model training.  
In summary, unlike existing approaches, the method introduced in this paper, termed FedCDAE-MN, places its primary emphasis on elevating the accuracy of mechanical fault diagnosis within the framework of few-shot learning across multi-source domain scenarios. Simultaneously, it prioritizes safeguarding user data privacy through personalized federated learning. Table 1 provides a comprehensive summary comparing our method with existing approaches.
## 4 Case Study: UBC's BRDF Expansion Project
### Understanding Specific Project Constraints and Challenges That Led to the Use of BIM
#### 4.2.2 Lack of Reliable As-Built Information  
Accurate as-built information is crucial in renovation projects for designing and constructing new sections. However, as for the BRDF expansion project, inaccurate as-built documentation of the original facility, including building drawings and manuals, caused substantial delays during construction. For instance,  
\"A couple of existing conditions that were found along the way like the communication ducts under the High Head Lab certainly delayed that scope of work. The conveyor pit conflicted with the existing underground conduits, and it also resulted and impacted the installation of the conveyors on the other end of the building\". --General Contractor's Project Manager  
These types of issues ultimately led to recapturing and modeling many building parts from scratch by the project team.
## 3 Case Study  
### Project Specification  
A high core rockfill dam, named PB, was selected as the case study to verify the feasibility and effectiveness of the proposed method, and a typical cross-section is shown in Figure 2. It is noted that the overburden of the dam foundation can be divided into four layers: the floated boulder and pebble layer (Q\({}_{3}^{2}\)), the pebble and gravel layer (Q\({}_{4}^{1-1}\)), the pebble layer with boulders (Q\({}_{4}^{1-2}\)), and the boulder and pebble layer (Q\({}_{4}^{2}\)). To study the effects of independent and joint sampling of material parameters on the stress and deformation of high core rockfill dams, a generalized plane model referring to the PB Hydropower Station was constructed, and the size information for the conceptual model of a high core rockfill dam was as follows: the dam height was 200 m, the water depth in front of the dam was 190 m, the crest width was 14 m, and the upstream and downstream slope ratio was 1:0.25. The dam body included upstream and downstream rockfill areas, transition layers, inverted flayers, and a core wall. The foundation included the overburden (thickness: 70 m) and the bedrock.  
The dam-foundation system was discretized into 4798 nodes and 2335 elements (see Figure 3). The X axis pointed downstream, while the Y axis extended upward. The upstream and downstream boundaries of the model were controlled by normal displacement methods, and the bottom boundary was constrained rigidly.  
Statistical Analysis of the Multi-Dimensional Joint Distribution Model for the Duncan-Chang Model Parameters
## 4 Simulation Verification
### Simulation of AC Sub-Microgrid Stronger than DC Sub-Microgrid  
At the initial moment, the AC and DC loads are both 60 kW, and DG1, DG2, and DG3 in the AC sub-microgrid output power, while only DG4 and DG5 in the DC sub-microgrid output power, and at this time, the source loads are balanced within the AC sub-microgrid and DC sub-microgrid. At 0.2 s, the DC loads increase to 80 kW, and the AC loads increase to 80 kW at 0.5 s. The simulation results are shown in Figure 12.  
Before 0.2 s, the AC frequency and DC voltage are rated, and the ILC transmission power is approximately equal to 0 due to the source-load balance inside the hybrid microgrid, which is in standby mode. During the 0.2-0.5 s period, the DC loads increase, and the load carrying capacity is weak due to only two DGs outputting power in the DC sub-microgrid. According to the conventional droop control regulation, the DC bus voltage is maintained at 587 V and the ILC transmitted power is about \(-\)7 kW, whereas with adaptive droop control, the DC bus voltage is maintained at 589 V and the ILC transmitted power is about \(-\)8.5 kW, which is 1.5 kW more than the conventional droop. The AC loads also increase to 80 kW after 0.5 s. At this time, the loads carried by the AC sub-microgrid and DC sub-microgrid are equal, while the DC sub-microgrid is weaker compared to the  
Figure 12: Simulation results showing that AC sub-microgrid is stronger than DC sub-microgrid. (**a**) Comparison of two droop control waveforms; (**b**) output power of hybrid AC-DC microgrid DGs.  
AC sub-microgrid. Therefore, the AC sub-microgrid continues to provide smaller power support to the DC sub-microgrid, and at this time, the transmission power is close to the power under both controls due to the smaller transmission power.  
Comparative Analysis: Against the DC power feedback adaptive droop [25], which reports 587 V DC voltage under a 20 kW load surge, our method sustains 589 V. The 60% shorter voltage recovery time (80 ms vs. 200 ms in [25]) validates superior dynamic performance when supporting DC subgrids.
###### Abstract  
The diffusion coefficient (DC) of CO\({}_{2}\) in brine is a key parameter in geological carbon sequestration and CO\({}_{2}\)-Enhanced Oil Recovery (EOR), as it governs mass transfer efficiency and storage capacity. This study employs three machine learning (ML) models--Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)--to predict DC based on pressure, temperature, and salinity. The dataset, comprising 176 data points, spans pressures from 0.10 to 30.00 MPa, temperatures from 286.15 to 398.00 K, salinities from 0.00 to 6.76 mol/L, and DC values from 0.13 to 4.50 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s. The data was split into 80% for training and 20% for testing to ensure reliable model evaluation. Model performance was assessed using R\({}^{2}\), RMSE, and MAE. The RF model demonstrated the best performance, with an R\({}^{2}\) of 0.95, an RMSE of 0.03, and an MAE of 0.11 on the test set, indicating high predictive accuracy and generalization capability. In comparison, GBR achieved an R\({}^{2}\) of 0.925, and XGBoost achieved an R\({}^{2}\) of 0.91 on the test set. Feature importance analysis consistently identified temperature as the most influential factor, followed by salinity and pressure. This study highlights the potential of ML models for predicting CO\({}_{2}\) diffusion in brine, providing a robust, data-driven framework for optimizing CO\({}_{2}\)-EOR processes and carbon storage strategies. The findings underscore the critical role of temperature in diffusion behavior, offering valuable insights for future modeling and operational applications.  
diffusion; machine learning; random forest; gradient boosting regressor; extreme gradient boosting; enhanced oil recovery 22 May 2025  
27 June 2025  
3 July 2025  
31 July 2025  
3
## 4 Conclusions  
ZnO:Eu:Na nanocrystals were synthesized from a simple solgel method. XRD, Raman and XPS analysis showed the incorporations of both Eu\({}^{3+}\) and Na\({}^{+}\) ions in the ZnO lattice without altering the structure. The PL study showed the role of Na\({}^{+}\) ions as sensitizers for the excitation of Eu\({}^{3+}\) ions. The process of excitation transfer from ZnO intrinsic defects and Na\({}^{+}\) ions was confirmed by PL lifetimes. The photocatalytic efficiency of ZnO:Eu:Na NCs for the degradation of RbB under sunlight irradiation was improved by further Na\({}^{+}\) doping. The ZnEuNa4 photocatalyst was almost stable after four runs and was the most effective with the high kinetic rate constant k = 21.16 \(\times\) 10\({}^{-3}\) min\({}^{-1}\). By testing the quenching agents, both superoxide and holes are suggested as the main active species in the presence of the ZnEuNa4 photocatalyst under sunlight irradiation. The proposed photocatalytic mechanism involves the capture of photogenerated electrons by oxygen vacancies due to longer recombination rates. We have shown that oxygen vacancies induce the reduction of Eu\({}^{3+}\) ions which result in the formation of further \(O_{2}^{-\bullet}\) and \(OH^{\bullet}\) radicals, which are the key agents for RbB decomposition.  
The synthesized ZnO:Eu:Na photocatalyst along with its high efficiency and stability is a potential candidate for decomposition of dyes. Further, the good PL features and the significantly longer lifetimes could be beneficial in some optoelectronic applications, such as red light-emitting devices.  
Conceptualization, W.B., R.N., A.B.G.T. and H.E.; Methodology, B.G., A.B.G.T. and H.E.; Validation, F.H.A. and H.E.; Formal analysis, R.N., B.G. and F.H.A.; Investigation, W.B.; Resources, R.N. and E.S.; Writing--original draft, W.B.; Writing--review & editing, B.G., A.B.G.T. and H.E.; Visualization, J.-M.S. and E.S.; Supervision, J.-M.S. and H.E. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R223), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors express their gratitude to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R223), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Furthermore, the authors are thankful to the Deanship of Scientific Research at University of Bisha for supporting this work through the Fast-Track Research Support Program.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Investigation of the Uvradiation Action on Polyesters PHB and PLA by a Method of IR Spectroscopy
The band at 1382 cm\({}^{-1}\) refers to the symmetric strain vibrations of the CH\({}_{3}\)-group [36]. The 1361 cm\({}^{-1}\) band is mixed and consists of deformation and the asymmetric band vibrations of the CH in CH\({}_{3}\) group. After 24 h of UV radiation, new bands at 1410 cm\({}^{-1}\) (Figure S10 and Table S11) and 3150 cm\({}^{-1}\) (Figure 6) are observed in the PLA spectrum. These bands, as in the case of PHB (Figure 5), can be attributed to -OH vibrations in the carboxyl group. During the same period, changes also occur in the 1753 cm\({}^{-1}\) band (Figure S12) related to the stretching vibrations of the carbonyl (C=O) group. Its broadening and the formation of a shoulder in the range of 1724 cm\({}^{-1}\) are observed, which indicate the formation of a free carboxyl group due to the destruction of the ester group under the influence of UV irradiation. Thus, based on a comparative analysis of the IR spectra of PHB and PLA, it can be concluded that, under the action of UV radiation, the destruction of the ester groups takes place, which is expressed in the appearance of intense bands characterizing the formation of new structural units. On the basis of obtaining the data, the formal mechanism of PHB  
Figure 6: FTIR spectrum of PLA films before (A) and after UV radiation during 2 (B), 5 (C), 24, (D), 144 (E) hours. and PLA destruction under the action of ultraviolet radiation can be presented by the following scheme (Figure 7). As such, the destruction of polyesters under the action of UV radiation occurs with the cleavage of the ester bonds. In this case, a free carboxyl group and an unsaturated bond in the \(\alpha\)-position to the ester groups are formed, leading to a decrease in the molecular weight of polymers, the consequence of which is the embrittlement of the polymer composition observed after 144 h of UV radiation.