esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of Aerodynamic Performance of Savonius Wind Rotor Using Straight-Arc Curtain
## 3 Numerical Simulation of the Straight-Arc Curtain  
### Geometric Model  
Here, a conventional Savonius wind rotor model is adapted from [14]. The geometric parameters and a schematic of the adopted model are shown in Figure 3. The present Savonius wind rotor with two semi-cylinders blades is a conventional type wind rotor ([PERSON] and [PERSON], 2008) [2]. Although there are many other shapes of the blades, the semi-cylinders-type is always applied for its simple shape and it is also easy to be made. The rotor model height (\(H\)) and diameter (\(D\)) are both 208 mm, the diameter of the upper- and lower-blade end plates (\(D_{0}\)) is 250 mm, the gap distance (\(e\)) is 16 mm, and the overlap ratio (\(e\)d) is about 0.15.  
Figure 1: Working principle of the conventional Savonius wind rotor.  
Figure 2: Working principle of the straight–arc curtain.  
According to the experimental data, the variation of the static torque coefficient is periodic, the maximum value of the static torque is obtained at rotor angles in the range 40-45\({}^{\circ}\), and the minimum value is obtained at rotor angles in the range 160-165\({}^{\circ}\)[15]. In addition, the wind rotor exhibits a negative static torque coefficient at rotor angles in the range 150-165\({}^{\circ}\). Based on the above analysis, four representative rotor angles (45\({}^{\circ}\), 90\({}^{\circ}\), 150\({}^{\circ}\), and 165\({}^{\circ}\)) are selected to study the effect of the straight-arc
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 3 Optimization Problem  
Real-world optimization problems, including those in the logistics domain, often have a very high computational complexity, cf. [39; 40]. More precisely, these problems are especially difficult to solve in worst-case scenarios, where they are often considered intractable for meaningful instance sizes, which is, in particular, the case in our scenario, where inputs from many parties are pooled.  
In the following we recap the Collaborative Freighter Delivery Problem (CFDP), which is known to be NP-hard [3]; we tackle it with a heuristic later in Section 4.
# AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping-Scoring-Modeling Approach
## 4 Discussion
Ten of the 1508 factors displayed statistically significant variations in positive and negative datasets separately. Compared with the known feature selection algorithms, these ten features are effective antimicrobial peptide descriptors that produce higher accuracy when used with the AMP-GSM approach. As seen in Table 8, all 10 features belong to the physico-chemical group. Additionally, when we removed the physico-chemical group from the dataset and run our approach, it was observed that accuracy and AUC values significantly decreased for both Gram-negative and Gram-positive datasets. We also used two other benchmark datasets in order to make a comparison between different approaches. In Dataset 2, there are 1778 AMPs and 1778 non-AMPs. Using the whole dataset with 10-fold MCCV, for some performance metrics, AMP-GSM outperformed the DNN model with LSTM and convolutional layers, as proposed in [33]. As seen in Table 9, we obtained higher performance metrics for sensitivity, accuracy, and AUC compared to the DNN model with LSTM and convolutional layers [33]. Another dataset that we analyzed (Dataset 3) was provided by [PERSON] et al. in [35]. This dataset consists of anti-inflammatory peptides (AIP). It includes 1258 AIPs and 1887 non-AIPs. Their model consists of a feature selection part with RF. Using Dataset3, we obtained higher performance metrics (99% accuracy, 100% AUC) compared with their method and traditional machine learning approaches, such as SVM and k-NN (as seen in Table 10). Hence, we can conclude that the novel approach developed in this study can be used to predict not only antimicrobial peptides, but also anti-inflammatory peptides by considering group characteristics. Our technique performs well and provides better categorization of AMPs based on different types of information (physico-chemical, sequence-based, etc.). However, AMPs can be hazardous and inefficient as a medicine, which is undesirable. Studies on the synthesis and modification of AMPs have shown that even small modifications can impact how well they work. This approach does not take into account the functional traits of AMPs, but instead, it can only identify AMPs. It is possible to undertake additional studies in accordance with the roles played by AMPs, which will improve our comprehension of their method of action and our ability to forecast their behaviors. Another issue regarding the design of AMPs is that toxicity, stability, and bacterial resistance must all be addressed concurrently in the rational design of AMP-based therapeutics [62]. To achieve this, it is essential to determine the key attributes that a peptide contains in order to be effective against various bacterial species. To rationally develop antimicrobial peptides that target certain bacteria, this study offers a feature selection method based on grouping that is specific to bacteria. It will be crucial to test our study using larger datasets active against bacteria.
# D2D-Assisted Adaptive Federated Learning in Energy-Constrained Edge Computing
###### Abstract  
The booming growth of the internet of things has brought about widespread deployment of devices and massive amounts of sensing data to be processed. Federated learning (FL)-empowered mobile edge computing, known for pushing artificial intelligence to the network edge while preserving data privacy in learning cooperation, is a promising way to unleash the potential information of the data. However, FL's multi-server collaborative operating architecture inevitably results in communication energy consumption between edge servers, which poses great challenges to servers with constrained energy budgets, especially wireless communication servers that rely on battery power. The device-to-device (D2D) communication mode developed for FL turns high-cost and long-distance server interactions into energy-efficient proximity delivery and multi-level aggregations, effectively alleviating the server energy constraints. A few studies have been devoted to D2D-enabled FL management, but most of them have neglected to investigate server computing power for FL operation, and they have all ignored the impact of dataset characteristics on model training, thus failing to fully exploit the data processing capabilities of energy-constrained edge servers. To fill this gap, in this paper we propose a D2D-assisted FL mechanism for energy-constrained edge computing, which jointly incorporates computing power allocation and dataset correlation into FL scheduling. In view of the variable impacts of computational power on model accuracy at different training stages, we design a partite graph-based FL scheme with adaptive D2D pairing and aperiodic variable local iterations of heterogeneous edge servers. Moreover, we leverage graph learning to exploit the performance gain of the dataset correlation between the edge servers in the model aggregation process, and we propose a graph-and-deep reinforcement learning-based D2D server pairing algorithm, which effectively reduces FL model error. The numerical results demonstrate that our designed FL schemes have great advantages in improving FL training accuracy under edge servers' energy constraints.  
federated learning; energy; device-to-device; graph learning +
Footnote †: journal: Applied Sciences
## Materials and Methods
### Modeling Future GPP Loss  
The methodology incorporates the GPP data obtained from the MODIS GPP dataset at 500 m resolution (MOD17A2H). The MODIS GPP data are derived using satellite-based reflectance data and incorporate a light-use efficiency model that estimates GPP by taking into account factors such as absorbed photosynthetically active radiation (APAR) and the efficiency with which plants use this radiation to fix carbon. To begin, the data preparation involves reading raster files for GPP and temperature data for the years 2000, 2005, 2010, 2015, and 2020 using the read\_raster function in Python. The data included the annual mean temperatures corresponding to each of these years. The code then projects an increase in temperature of 3 degrees Celsius and a 10% increase in precipitation for future scenarios,as suggested by [19]. The GPP response modeling is carried out using the calculate\_gpp function, which calculates GPP based on the temperature and precipitation inputs. This function assumes a simple linear relationship where GPP decreases with an increase in both temperature and precipitation. In the simulation step, our code simulates the changes in GPP from the year 2000 to 2100, first interpolating the mean temperature for each year and then calculating GPP based on this interpolated temperature and the increased precipitation. This process is repeated for several temperature variations up to the projected future temperature of 2100. Finally, the visualization step involves plotting the simulated GPP data for the selected years, displaying how GPP might vary with different temperature conditions. Based on the information provided in study [22], the relationship between GPP, temperature (T), and precipitation (P) can be represented as linear equations. However, these are simplified representations and the actual relationships may be more complex and nonlinear.  
For crop and grassland ecosystems, the relationship can be represented as follows:  
\[\text{GPP}_{\text{crop/grassland}}=a_{1}+b_{1}\times P-c_{1}\times T \tag{4}\]  
In these equations,  
P represents precipitation.  
T represents temperature.  
\(a_{1},\ b_{1},\ c_{1}\) are constants that would need to be determined through regression analysis based on actual data.
## 4 Discussion  
The results were comparatively analyzed in section of the results for the FC system and the FC/renewable HPS using the strategies sFF, air-PFC, fuel-PFC, and air/fuel-PFC; here, we summarize and discuss these further in the frame of the working hypotheses.  
The fuel consumption using the strategies sFF, air-PFC, fuel-PFC, and air/fuel-PFC for the FC system and the FC/renewable HPS is summarized in Table 5.  
It is worth mentioning that the lowest fuel consumption was obtained using the air/fuel-PFC strategy for both FC-based power systems and then by using the strategies air-PFC, fuel-PFC, and sFF (see the first two rows of Table 5).  
In addition, it should be noted that the difference in the fuel consumption for the FC system and FC/renewable HPS, \(Fuel_{T(strategy)}-Fuel_{T(strategy)}^{Renewable}\), represents about 57% of the fuel consumption of the FC system using that strategy (see the last row of Table 5).  
This difference results from operating the FC system from the FC/renewable HPS at low power due to contribution of RES power on the load demand. The profiles of the load demand and RES power shown in Figure 2 have a MV of 5 kW and about 2.5 kW, justifying the 57% reduction in the fuel consumption for the FC system and FC/renewable HPS.  
The fuel economy strategies for the FC/renewable HPS compared to the FC system are better by about 0.7% (see last row of Table 6) due to the same reason (FC system operates at lower power due to available renewable energy on the DC bus).  
Figure 15: Oxygen excess ratio.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter [unit] Strategy** & **sFF** & **Air-PFC** & **Fuel-PFC** & **Air/Fuel-PFC** \\ \hline \(Fuel_{T(strategy)}\) [L] & 286.5 & 268.1 & 270.8 & 250.4 \\ \(Fuel_{T(strategy)}^{Renewable}\) [L] & 107 & 115.4 & 116.6 & 123.6 \\ \(Fuel_{T(strategy)}-Fuel_{T(strategy)}^{Renewable}\) [L] & 143.40 & 152.70 & 154.20 & 162.90 \\ \(\left(Fuel_{T(strategy)}-Fuel_{T(strategy)}^{Renewable}\right)/Fuel_{T(strategy)}\) [\%] & 57.27 & 56.96 & 56.94 & 56.86 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the fuel consumption using the strategies sFF, air-PFC, fuel-PFC, and air/fuel-PFC for the FC system and FC/renewable HPS.
# Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost
## 4 Application Results and Analysis
### Analysis of Data Filling Methods  
The effectiveness of the method for filling in missing data in DGA samples should be based on the accuracy of the diagnostic results. Therefore, this paper combines statistical methods, kNN [27], ridge regression (Ridge) [28], CART and RFR to analyze the diagnostic results, as shown in Figure 5.  
The diagnostic accuracy of the XGBoost model with complete data is 82.3%. The results show that RFR and CART methods are better than kNN, Ridge and statistical methods at filling in the missing values of DGA data, which indicates that the relationship between DGA characteristic gases is non-linear. In addition, the accuracy of the diagnostic results is 74.5% when the missing data are not treated in any way. The accuracy is slightly reduced  
Figure 4: Flowchart fault diagnosis model based on TPE-XGBoost.  
Figure 5: Comparison of the effect of filling methods on diagnostic results.  
when missing values are filled in using zero or mean values. Compared to the missing data, the diagnostic effect of the data filled by the RFR method is improved by 5.5%, and its accuracy rate reaches 80%, indicating that the RFR method can effectively restore the information of missing data in DGA samples.
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 1 Introduction
We observe that the traditional parallel implicit assembly methods all perform the three key steps for automatic hole-cutting in a coupled manner, which can result in enormous communication data for large-scale overlapped grids and decrease the parallel efficiency. To address the aforementioned issues, we propose a parallel implicit assembly algorithm for overlapping grids, and our contributions are as follows:  
* We propose a parallel implicit assembly method that employs a two-step nodes classification scheme to accelerate the hole-cutting operation. * We implement the proposed method into a convenient library called overlapping grid sewing machine (OGSM) for integration. * We validate the parallel assembly capability of overlapping multi-component grids with a seven-sphere model. * We validate the parallel computing efficiency and interpolation accuracy of OGSM with a multi-body trajectory prediction case. The rest of the paper is structured as follows. Section 2 presents the implicit assembly algorithm of overlapping grids. Section 3 demonstrates our proposed parallel implicit assembly algorithm, followed by the implementation of the OGSM library. The results of the numerical experiments are presented in Section 5, and Section 6 gives the conclusion and our future work.
## 4 Results and Discussions
### Adsorption Equilibrium Studies of Methylene Blue (MB)
The results of this analysis are illustrated in Table 3.  
Much research has reported that MB adsorption on montmorillonite, phosphorous rock and palygorskite correlate well with the Langmuir isotherm and provided a good fit with the experimental data than other isotherms [42; 43; 44; 45]. As expected, the result suggests that the MB adsorption process by clay minerals was a single layer adsorption and that the maximum monolayer adsorption capacities were found to be 241.96, 198.36, 168.59 and 93.42 mg/g, for S\({}_{0}\), S\({}_{1}\), S\({}_{2}\) and S\({}_{3}\), respectively. It is observed that the maximum quantity of MB adsorbed on raw material (S\({}_{0}\)) was reduced by about 60% for sample activated by strong conditions (S\({}_{3}\)). The uptake of methylene blue by clays is strongly related to cationic exchange. As reported by [PERSON] et al. [46] a complete balance between MB adsorbed and desorbed exchanged cations is needed to account for the mechanism of cationic exchange. Therefore the uptake of MB is accompanied by dual mechanism of ion exchange as well as adsorption. Overall, the quantities of desorbed exchangeable cations are linearly proportional to those of adsorbed MB [45]. Moreover, the decrease in adsorption ability of MB for activated samples (S\({}_{1}\), S\({}_{2}\) and S\({}_{3}\)) could be a consequence of the collapse of the clay lattice to some extent of acid activation. A similar result was found by [PERSON] et al. [18]; when more concentrated acids were used, the crystallinity of the clay minerals deteriorated. The decrease in MB uptake could also be attributed to the electrostatic repulsion between the positively charged surface of acid activated clays and the positively charged dye molecule in the acidic medium, in addition to the competitive adsorption between the H\({}^{+}\) ions and dye cations to reach the surface.
## 3 The Mesotheliomagenic Activity of CNT: A Remaining Issue
### Advances in Understanding Mesothelioma Development
#### 3.2.1 Direct Effects and DNA Damages  
Carcinogenicity of asbestos is related to their physical and chemical properties. Once fibers reach the pleural or peritoneal cavity, longer particles accumulate and are directly in contact with mesothelial cells [22,55,56]. Asbestos generate reactive oxygen species (ROS) and induce ROS production by exposed mesothelial cells. ROS trigger genomic instability and mutations by interacting with mesothelial cells [57]. These oxidant molecules also activate various signalling pathways, inducing transformed cell proliferation and survival [22,55,56]. ROS are therefore involved in the initiation, promotion and progression of cancer, which represent the three stages of the carcinogenic process [58].  
Asbestos are known to penetrate cell membrane and interact with intracellular molecules resulting in direct (or primary) genotoxicity that include DNA strand breaks, mutations and chromosomal aberrations [57]. Recent studies showed that CNT induce chromosomal disruptions, fragmentations and translocations [59,60] and their nuclear deposition results in clear epigenetic alterations [61,62,63]. The quenching capacity of CNT is also involved in their primary genotoxic effects [39] (Figure 1).  
However, the literature concerning the direct interaction of asbestos or CNT with DNA is contradictory. The team of [PERSON] [64] states that this process does not exist, unlike most other chemical carcinogens. The non-clastogenicity of the CNT observed by [PERSON] suggest that the CNT may not directly interact with DNA [65].
## 4 Conclusions  
In the present study, it was found that FY-07 did not significantly inhibit the growth and metabolism of WJ-1 under sufficient nutrition conditions but improved the ability of WJ-1 to degrade the high carbon chain crude oil. In fact, WJ-1 promoted the growth and metabolism of FY-07 to some extent. The results indicate that WJ-1 and FY-07 exhibited a good compatibility. Physical simulation oil displacement experiments show that WJ-1 and FY-07 combined flooding improved the oil recovery  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Tested Slug**} & \multirow{2}{*}{**Porosity (\%)**} & \multirow{2}{*}{**Initial Oil**} & \multicolumn{3}{c}{**Oil Recovery (\%)**} \\ \cline{5-6}  & & & & **Saturation (\%)** & & **Water Flooding** & **Final** & **EOR** \\  & & & & **Oil Recovery** & **Recovery** & **Efficiency** \\ \hline
1 & Control & 28.2 & 78.1 & 18.4 & 21.3 & 2.9 \\
2 & FY-07 & 27.5 & 78.7 & 21.2 & 31.6 & 10.4 \\
3 & WJ-1 & 28.0 & 77.9 & 18.6 & 26.5 & 7.9 \\
4 & FY-07+WJ-1 & 28.4 & 77.4 & 20.9 & 38.3 & 17.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the core-flooding tests.  
Figure 7: Contents of different carbon number of crude oils before and after microbial treatment.  
by 17.4%, which was higher than the individual effect of the two strains. Therefore, compound flooding with the polymer-producing bacteria FY-07 and the surfactant-producing bacteria WJ-1 has a good application prospects for enhancing the oil recovery in heterogeneous reservoirs.  
[PERSON], [PERSON], and [PERSON] designed and discussed the experiment; [PERSON] provided the strain _E. cloace_ FY-07; [PERSON] conducted the experiment; [PERSON] and [PERSON] analyzed and discussed the results; [PERSON] wrote the paper; [PERSON] gave the help and suggestion in the revised manuscript.  
This research was supported by the National Natural Science Foundation of China (NO.2018)Q1067), the Natural Science Basic Research Program of Shaanxi Province (NO.2019)M-396) and the Research Foundation Project of Xi'an Aeronautical University (NO.2018 KY0101). We thank the support from CNPC Key Laboratory of Enhanced Oil Recovery.  
The authors declare no conflict of interest.
## 2 Simulation Method  
When incident waves are propagated and reflected by the GMR structure via normal incidence, the waves can be scattered because of the high refractive index of the grating layer. Subsequently, the incident waves are coupled to the leaky mode and propagated through the waveguide layer under resonance conditions [17; 18; 19; 20]. Generally, the grating and waveguide layers can have different refractive indices to satisfy the GMR condition. The schematic details are shown in Figure 1. Particularly, from the effective medium theory, the grating layer in the GMR structure has the following effective refractive index [10; 21]:  
\[n_{eff}=\sqrt{Fn_{H}{}^{2}+(1-F)n_{L}{}^{2}} \tag{1}\]  
where \(n_{H}\) and \(n_{L}\) are the respective refractive indexes of the grating region and air, and \(F\) indicates the filling factor, which can be defined as the ratio of the length of grating particles to the period, \(\Lambda\). Figure 1 shows the simulated geometry of the GMR structure in our studies. The symbols \(d_{g}\) and \(d_{wg}\) denote the thicknesses of the grating and waveguide layers, respectively. We extracted the spectrum of the GMR structure in the range from 700 to 1000 GHz for all results, using a commercial FDTD program, where the periodic boundary condition used for the horizontal direction is shown in Figure 1, and the perfect matched layer boundary condition was used for the vertical direction. In our FDTD simulation, the unit cell was periodically extended in one-dimensional direction on the surface. The refractive index of the substrate was assigned a constant value of 1.52, which is the same as that of polyethylene. The absorption coefficient of polyethylene is significantly lower than that of other polymers [22]. The thickness of the grating layer was set to 0.1 mm, and the waveguide was considered to have a thickness of 0.279 mm. In addition, the width of the grating layer depended on the filling factor and the period. The input THz waves were set to TM (Transverse Magnetic) mode plane waves, and all the simulated results were obtained through reflectance mode detection.  
Figure 1: Simulated structure of the GMR device of (**a**) rectangular and (**b**) spherical shapes.