esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### RT-qPCR Analysis of the TaTS Transcript Profiles  
The total RNAs were extracted using the FastPure Plant Total RNA Isolation Kit (Vazyme, Nanjing, China) from the cultures of ACCC 33109 treated with or without _Foc_. The experiment was performed once for 5 days with three biological replicates. Each biological replicate consisted of two ACCC 33109 cultures independently treated with _Foc_ or blank PDA. The RNAs were treated by DNase I (Ambion, Foster City, CA, USA), followed by purification using the RNeasy Kit (Qiagen, Valencia, CA, USA). The concentration and quality of the purified RNAs were assessed using the A\({}_{260}\)/A\({}_{280}\) and A\({}_{260}\)/A\({}_{230}\) absorbance ratios and gel electrophoresis. The RNAs (1 \(\upmu\)g) were reverse transcribed to cDNAs in a 20 \(\upmu\)L reaction using the HiScript II 1 st Strand cDNA Synthesis Kit (Vazyme).  
The qPCR primers for the 15 _Tasts_ genes and the _\(\beta\)-tubulin_ gene as the reference were designed using Primer3 web v.4.1.0 ([[http://primer3.ut.ee/](http://primer3.ut.ee/)]([http://primer3.ut.ee/](http://primer3.ut.ee/)), accessed on 18 March 2025) and are listed in Supplementary Table S3. The primer specificity was tested using the genomic DNA and the cDNAs of 3-day-old ACCC 33109 cultures with _Foc_ induction as templates. Each PCR reaction with three technical replicates was performed using the ChamQ SYBR qPCR kit (Vazyme) consisting of 12.5 \(\upmu\)L 2\(\times\) PCR master mix, 1 \(\upmu\)L each of forward and reverse primers (10 \(\upmu\)M), 2 \(\upmu\)L diluted DNA/cDNAAs (1:5-10 dilution), and 8.5 \(\upmu\)L UltraPureTM DNase/RNase-free distilled water. The qPCR conditions were as follows: one cycle of 10 min at 95 \({}^{\circ}\)C, 40 cycles of 30 s at 95 \({}^{\circ}\)C, 30 s at 60 \({}^{\circ}\)C, and 45 s at 72 \({}^{\circ}\)C, one cycle of 1 min at 95 \({}^{\circ}\)C and 30 s at 55 \({}^{\circ}\)C, and a final ramp to 95 \({}^{\circ}\)C to check the primer dimerization and nonspecific amplification. RNase-free distilled water was used as blank controls. The relative expression levels of the _TaTS_ and _\(\beta\)-tubulin_ genes were calculated using the ViATM7 software (Applied Biosystems, Foster City, CA, USA) and the Comparative \(\Delta\Delta\)C\({}_{\mathrm{t}}\) method [33].
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 6 Future Directions and Real-World Challenges
### Cold Start Problem  
The cold start problem is a challenge faced by FRSs when providing accurate and personalized recommendations. This problem arises when there are no existing data available to predict user preferences accurately. Without these data, the system cannot make accurate recommendations and may default to providing generic suggestions. As a result, users may be less likely to engage with the system, leading to a decrease in customer satisfaction:  
* **Future directions**: These may include collecting more user data, such as preferences, likes, and dislikes, and implementing techniques such as collaborative filtering that allow the system to draw connections between similar users and their preferences. Moreover, techniques such as deep learning may be used to understand user behavior better. The FRS may also benefit from integrating external data sources, such as social media, from personalizing the user experience further.
* **Real-world challenges**: These include protecting user privacy, balancing accuracy and complexity, and ensuring scalability. To ensure user privacy, FRSs must be designed with privacy-preserving techniques in mind. The complexity of the model must be balanced with its accuracy, as complex models may be difficult to interpret and may not be able to predict user preferences accurately. Finally, the system must be able  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**System** & **Security Protocols** & **Limitations** & **Issues and Challenges** \\ \hline Content-Based Filtering & Secure aggregation & Data privacy and security concerns & Develop more scalable and efficient secure aggregation methods, improve data sharing protocols \\ \hline Knowledge-Based Systems & Access control and authorization & Limited scalability, reduced accuracy & Lack of data for knowledge-based models \\ \hline Collaborative Filtering & Access control, authentication & Data privacy and security concerns & The reliability of data between different parties \\ \hline Context-Aware Recommendation & Secure aggregation, authentication, and authorization & High computational overhead & Lack of data for context-aware models \\ \hline Hybrid Recommendation & Access control, authentication, authorization & High computational overhead, reduced accuracy & Improve scalability, develop secure aggregation methods, explore privacy-preserving MPC techniques \\ \hline \hline \end{tabular}
\end{table}
Table 6: Current security protocols, limitations, issues, and challenges in FRSs.  
to handle large amounts of data when scaling, as more data will allow for more accurate predictions.
## 10 Simulation Results and Discussion
### Energy Consumption  
In Figure 10, the energy consumption of appliances in the WDO algorithm is low during \((0\to 3)\) h, while, appliances consume more energy with the PSO algorithms. During high peak hours \((7\to 10)\),  
Figure 8: Electricity cost comparison of using the WDO and PSO algorithms.  
Figure 9: Electricity cost comparison of using the knapsack WDO (K-WDO) and K-PSO algorithms.  
appliance energy consumption in the PSO case is comparatively low with respect to the WDO algorithm. During hours \((10\to 24)\), the average energy consumption of both the scheduled and unscheduled cases is the same. While, Figure 11 shows some variations in energy consumption during high and low peak hours, during low peak hours \((0\to 7)\) and \((11\to 25)\), the K-WDO algorithm schedules most of its appliances for a low electricity bill; while electricity prices during \((6\to 10)\) h are high and the K-PSO algorithm schedules a greater number of appliances in these time slots. On the other hand, the K-WDO algorithm does not turn on any appliance during high peak hours and utilizes the low peak time slots to complete working cycles. In conclusion, we observed that the K-WDO algorithm more effectively reduces the energy consumption by optimally scheduling the appliances in low and mid-peak hours.  
Figure 11: Energy consumption comparison of the K-PSO and K-WDO algorithms.  
Figure 10: Energy consumption comparison of the PSO and WDO algorithms.
# Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
###### Abstract
Different from traditional preventive maintenance, PdM is capable of dynamically predicting when maintenance will be nee  
Figure 1: The relationship between remanufacturability and predictive maintenance. the static average or expected life statistics. Moreover, some studies show that PdM could help improve remanufacturability and reduce the remanufacturing cost [18]. With the development of industrial 4.0, the PdM techniques have been widely applied to many real-world industrial applications combined with historic data, models, and domain knowledge [19]. For complex equipment, condition monitoring (CM) is the most effective way to evaluate performance based on practical data [20]. The traditional CM methods extract the effective features from the original data, then build a health index to identify the condition of the machine [21; 22; 23]. The intelligent CM methods aim at directly assessing the state of the machine from the data, which have been made available with the rapid development of machine learning techniques [24; 25; 26; 27]. As the most critical part of predictive maintenance, remaining useful life predictions are designed to estimate the future state of a component based on current and historical data [28; 29]. Data collection and processing and condition monitoring are the foundation for supporting the PdM. Dynamic maintenance scheduling can only work after acquiring the prediction of future states. Generally, there are three types of methods to carry out RUL prediction, including model-based, data-based, and hybrid-based approaches [30; 31; 32]. Model-based methods use the physical model of the degradation process, which is extracted from the expert knowledge about the life cycle of a given product. The main algorithms to implement the model-based approaches are particle filtering [33; 34] and its variants [35]. However, an accurate knowledge model could barely be derived for complex equipment. In contrast, data-based methods do not rely on knowledge of the degradation process but will learn the model relationship from data. Machine learning methods are used to learn the degradation prediction functions, mainly including general neural network [36], convolution neural network [37] and recurrent neural network [38]. Nevertheless, these approaches do not match the requirement of the dynamic degradation process; when new data have been collected, they need massive time to retrain the model. The principle motivation for this paper is to use the hybrid-based approach, which utilizes both model-based and data-based approaches for developing a predictive maintenance framework for remanufacturability. The proposed method and detailed specifications are described in Section 2. Section 3 shows detailed experiments and analysis results. The conclusions are included in Section 4.
## 3 Approach and Analysis: Architecture and Methodology
### Methodology: Machine Learning-Based Approach  
The methodology for analyzing the sound contexts was composed through the feature extraction of the sound elements. After the feature extraction, sound classification was carried out based on the context of the dog sound. In the proposed methodology, classification of the dog sounds was based on the feature extraction and contextual information. Both the dog species were placed in similar situations; for most of the situations encountered, Rajapalayam Hound and Kombai Hound made sounds with an identical pattern.  
Huge differences in the barking sound were noticed in two extreme conditions--when an owner or stranger enters the testing compound, and also when the dog is sitting alone all by itself at the house, or is in an open environment, away from the house. The acoustic features of the dog sounds were analyzed and annotated in order to identify the behavior of the dog. Through the proposed method, the pattern of the dog sounds due to environmental changes could be determined by the humans in the houses. Figure 3 depicts the proposed methodology for recording and analyzing the different banks of the dog. The recorded dog barking sound was framed to identify the barking, because a single recorded sound contains a series of barking sounds.  
Sound acquisition involves acquiring the dog sounds by positioning the microphone. The acquired sounds in different scenarios were analyzed in order to identify patterns in the dog barking. Framing is important and allows the algorithm to extract the unique features from the frames of the sounds. Therefore, framing segments the signals into several frames, and this is very important considering that the signals are not static and change rapidly. Fast Fourier transform was used to separate the banks based on their sequence. The sequence of dog barks recorded was used in order to understand the complete meaning or the reason for the bark made by the dogs. Mel-Frequency Cepstral Coefficients (MFCCs) were used for the determination of the spectrum of sounds exhibited, which were similar to the human auditory responses [45; 46].  
MFCCs were used for distinguishing between dog barks based on the frequency levels at which they had been made. Based on the MFCC coefficients obtained, sound classification of the dog was made to identify the barking pattern. Sound patterns observed during different scenarios were taken into considerations. By using spectrogram analysis, variation in the dog sounds identified and sound patterns were identified.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 3 Fouling and Concentration Polarization in Submerged HF Systems
### Fouling in Submerged Dead-End Filtration  
In dead-end filtration, tangential shear is absent and particles are convected by the permeate flow to deposit on the membrane surface, thereby forming a growing cake layer with time. Physical cleaning approaches are typically implemented periodically for the effective removal of the fouling layer in order to prolong the filtration process and membrane lifespan in submerged membrane systems. Such approaches include relaxation (i.e., intermittent cessation of permeation), backwashing (i.e., reversal of permeate flow through the pores), and air backwashing with or without air scouring [88; 89; 25; 90; 91]. Filtration duration, backwash and relaxation durations, and backwashing flowrate are important parameters in the fouling mitigation of submerged HF membranes [92; 93; 26; 90]. However, a major challenge in the application of these techniques is that the imposed permeate fluxes have to be elevated to maintain a given water production, which in turn could result in a higher fouling rate [94]. More details on backwashing and relaxation can be found in Section 7.
###### Abstract  
Advances in satellite technologies are increasing the power density of electronics and payloads. When the power consumption increases within a limited volume, waste heat generation also increases and this necessitates a proper and efficient thermal management system. Mostly, micro and nanosatellites use passive thermal control methods because of the low cost, no additional power requirement, ease of implementation, and better thermal performance. Passive methods lack the ability to meet certain thermal requirements on larger and smaller satellite platforms. This work numerically studies the performance of some of the passive thermal control techniques such as thermal straps, surface coatings, multi-layer insulation (MLI), and radiators for a 6U small satellite configuration carrying a mid-wave infrared (MWIR) payload whose temperature needs to be cooled down to 100K. Infrared (IR) imagers require low temperature, and the level of cooling is entirely dependent on the infrared wavelengths. These instruments are used for various applications including Earth observations, defence, and imaging at IR wavelengths. To achieve these low temperatures on such instruments, a micro-cryocooler is considered in this study. Most of the higher heat dissipating elements in the satellite are mounted to a heat exchanger plate, which is thermally coupled to an external radiator using thermal straps and heat pipes. The effects of the radiator size, orbital inclinations, space environments, satellite attitude with respect to the sun, and surface coatings are discussed elaborately for a 6U satellite configuration.  
thermal control systems; nanosatellite; micro-satellite; heat pipes; radiators; thermal straps; infrared imagers Article
# Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements
## 3 Derivation of Probability-Stress-Life Curves
### Result and Discussion  
The P-S-N curves of rib-to-deck joints were first derived using the traditional assumption with the 97.7% survival rate, in which the uncertainty in estimators was not considered. The result of the two types of specimens is shown in Figure 8a,b, in terms of nominal stress and hot spot stress, respectively.  
Figure 8: P-S–N curve under the survival rate of 97.7%: (a) nominal stress and (b) hot spot stress.  
Generally, the characteristic fatigue strength at 2 million cycles, called the \"FAT\" value, can be utilized to category the class of fatigue-critical details [20; 22]. Based on the above analysis, the FAT value of the CU specimens was 79.7 MPa in terms of nominal stress and 104.2 MPa in terms of hot spot stress. It is interesting to note that the derived curve is slightly higher than the curve proposed in Eurocode 3 [20], i.e., the FAT 70 curve in nominal stress and the FAT 100 curve in hot spot stress. However, the improvement in welding technology can account for the difference since the curve in Eurocode 3 is based mainly on fatigue test data obtained before the 1980s. For the TEU specimens, the FAT values were 99.2 and 126.2 MPa in terms of nominal stress and hot spot stress, respectively. Compared with CU specimens, the FAT value in TEU specimens increased by 24.5% in nominal stress and 21.1% in hot spot stress, further indicating the effectiveness of TEUs in enhancing the fatigue strength of rib-to-deck joints.  
At the same time, the IIW method was also applied to derive the P-S-N curve of rib-to-deck joints, under the survival rate of 95% and tolerant limit of the 75% for the estimators. The derived curves are shown in Figure 9.  
Compared with the CU specimens, the FAT value in the TEU specimens were 34.1% and 28.5% higher in terms of nominal stress and hot spot stress, respectively. It is worth noting that the FAT values in Figure 9 are notably lower than that in Figure 8, even if the survival rate is reduced from 97.7% to 95%. The comparison between Figures 8 and 9 suggests that a more conservative P-S-N curve can be obtained by the IIW method, since the uncertainty in estimators is considered. To this end, it is suggested that the IIW method-based P-S-N curves in the fatigue design of rib-to-deck joints be used. After rounded off with engineering experience, for rib-to-deck joints in OSDs using TEUs, the FAT 90 and FAT 115 curves are proposed in terms of nominal stress and hot spot stress, respectively. Meanwhile, for the joints in OSDs using CUs, the FAT 70 curve is proposed under the nominal stress and while the FAT 90 curve is suggested when using the hot spot stress approach.
## 4 Numerical Results
### Research Insights  
The simulation results demonstrate that the proposed MAPPO-based joint caching and computation offloading scheme performs excellently in UAV-assisted vehicular networks. Compared to baseline strategies such as MADDPG, MAAC, Greedy, and Random, the MAPPO algorithm achieves significant advantages in key performance metrics, including cumulative reward, cache hit rate, system efficiency, and total task delay, while also effectively controlling energy consumption. Furthermore, through the analysis of different Batch Sizes and Learning Rates, we identified suitable hyperparameter settings for this scheme. The analysis of different numbers of UAVs also validated the effectiveness and robustness of the proposed scheme to changes in system scale. These results fully demonstrate the feasibility and superiority of applying multi-agent deep reinforcement learning to the joint caching and computation offloading problem in UAV-assisted vehicular networks.  
Figure 11: Energy consumption (mJ) vs. offloaded computation (Mcycles).
# Evaluating Soil Bacteria for the Development of New Biopreparations with Agricultural Applications
## 3 Results
### Antagonistic Properties Against Phytopathogens  
The antimicrobial activities of the five selected strains (_B. subtilis_, _B. licheniformis_, _B. velezensis_, _P. amylolyticus_, and _P. megaterium_) were evaluated against phytopathogens. All the strains exhibited antimicrobial activity against at least three of the tested phytopathogens (Figure 6).  
The strains _B. licheniformis_, _B. velezensis_, and _B. subtilis_ exhibited antimicrobial activity against all the tested phytopathogens. _B. velezensis_ and _B. subtilis_ exhibited the largest zones of inhibition. _B. velezensis_ inhibited _P. exigua_ with a mean zone of 40.0 \(\pm\) 1.0 mm, as well as _F. graminearum_ and _F. avenueum_ with a mean zone of 40.0 \(\pm\) 0.0 mm. _B. subtilis_ inhibited _F. graminearum_ with a mean zone of 41.7 \(\pm\) 6.0 mm, as well as _C. coccodes_ and _P. exigua_ with a mean zone of 40.3 \(\pm\) 0.6 mm. _P. amylolyticus_ showed antimicrobial activity (inhibition zones \(\geq\) 30 mm) against the maize-related pathogens _F. graminearum_, _F. proliferation_, and _F. avenueum_, but did not exhibit antimicrobial activity against the other phytopathogens. _P. megaterium_ exhibited the lowest level of antimicrobial activity. Although it showed activity against four of the tested phytopathogens, the growth inhibition zones were \(\leq\)10 mm.  
Figure 5: Decomposition of maize crop residues at 20 \({}^{\circ}\)C in non-sterilized horticultural substrate moistened with distilled water (control), with the addition individual microorganisms and a mixture of microorganisms, on a five-point quality scale (5—non-decomposed residues, 1—completely decomposed residues). NIR was calculated at a significance level of \(p=0.05\).
## 3 Results
### Relationship between Leaf Nitrogen Concentration and SPAD Readings  
A regression analysis between SPAD values and leaf nitrogen content showed a consistent relationship (R\({}^{2}\) = 0.68; \(p\) < 0.001) as given in Figure 5. The leaf nitrogen content and the relationship with the SPAD readings are in accordance with the study by [58]. However, there is a variation in the significance of the model with other studies. It is due to the continuous observation of the SPAD, and laboratory analysis of nitrogen compared to others. On the other hand, SPAD values and the greenness of the leaf are significantly correlated, alternately depending on the rate of fertilizer application.
## 4 Discussion
### Limitations and Future Research Directions  
This study has some limitations. Although we conducted an a priori power analysis to determine the sample size based on previous studies' sample sizes, the small sample of 15 young elite male soccer players may limit the generalizability of the findings. For future research, expanding the sample size and youth female athletes would enhance the generalizability of the results. Additionally, this study had a randomized crossover design, which is robust, but it only involved three sessions across a 2-week period. A longer intervention period or additional sessions with more sprints or sets could provide a more comprehensive understanding of the effects of short rest intervals on resisted sprint performance. Lastly, we did not measure the changes of muscle activation patterns or aerobic and anaerobic metabolism. Future research that includes these measurements could provide deeper insight into why short rest intervals did not affect performance as hypothesized.