esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of Aerodynamic Performance of Savonius Wind Rotor Using Straight-Arc Curtain
## 1 Introduction
[PERSON] and [PERSON] (2008, 2010) used a curtain design to increase the performance of a conventional Savonius wind rotor [1; 2]. This curtain has been placed in front of the rotor, designed to prevent the negative torque on the convex blade of the rotor, and experiments have been carried out to test its performance. The results show that a significant increase can be achieved in the rotor performance by means of the curtain design. [PERSON] et al. (2012) experimentally studied different geometries on the performance of Savonius wind turbine in order to determine the most effective operation parameters [17]. They found that the rotors without overlap ratio are better in operation than those with overlap; the power coefficient increases with rising the aspect ratio; the two-blades rotor is more efficient than the three and four ones. [PERSON], [PERSON] and [PERSON] (2015) experimentally investigated the effect of number of blades on the performance of the model of Savonius-type wind turbine [18]. The results show that Savonius wind turbine with three blades has the best performance at high tip speed ratio. The highest tip speed ratio is 0.555 for wind speed of 7 m/s. [PERSON] et al. (2015) proposed two different approaches to enhance the performance: one is based on the development of innovative airfoil-shaped blades, the other one concerns the use of a new curtain system, self-orienting relative to the wind direction [19]. Both of the two methods work well. [PERSON] and [PERSON] (2016) studied the performance and shape characteristics of a helical Savonius wind turbine at various helical angles [6]. The maximum power coefficient (about 0.15) occurred at the twist angle of 45\({}^{\circ}\), whereas it decreased by 25.5% at 90\({}^{\circ}\) and 135\({}^{\circ}\). Regarding the torque coefficient values at various azimuths, they also found that the peak-to-peak values in the profiles for 90\({}^{\circ}\) and 135\({}^{\circ}\) were less than those for 0\({}^{\circ}\) and 45\({}^{\circ}\). [PERSON] et al. (2019) introduced an upstream deflector and downstream baffle around a Savonius wind turbine and achieve a much higher power coefficient than the conventional Savonius turbine [20]. In the present study, a straight-arc curtain, placed in front of the Savonius wind rotor to improve its aerodynamic performance, is tested. The static torque, mean torque coefficients, and mean power coefficient are analyzed using numerical simulation.
# On Beamforming with the Single-Sideband Transform
## 3 Signal Model and Beamforming
 \tag{38}\]  
in which \(\bar{\mathbf{D}}^{n}_{m,k}\) is an \(L_{Y}\times L\) Toeplitz matrix with \(L=L_{D}+L_{Y}-1\), and \(\bar{\mathbf{x}}^{n}_{1,k}[I]\) is an \(L\times 1\) vector of our desired signal  
\[\bar{\mathbf{D}}^{n}_{m,k} =\begin{bmatrix}\mathbf{d}^{n}_{m,k}\ \ \ \ 0&\cdots\ \ \ 0\\ 0&\mathbf{d}^{n}_{m,k}\ \ \ \ 0\\ \vdots&\vdots\ \ddots&\vdots\\ 0&0\ \cdots&\mathbf{d}^{n}_{m,k}\
## 3 Results
### Complexity Analysis
#### 3.1.5 Algorithms for K and Double Letter Detection  
K Letter Detection:
* Description: Involves monitoring conditions for three subsequent frames after detecting a static \"P\" to identify a \"K\".
* Complexity: The complexity is \(O(1)\) for each frame, resulting in an overall \(O(N)\) complexity for processing the entire sequence of frames in a video.  
Double Letter Detection (rr, ll):
* Description: Monitors wrist movement over a sequence of frames to confirm horizontal displacement and minimal vertical movement.
* Complexity: Similar to K detection, the per-frame complexity is \(O(1)\), making the total complexity \(O(N)\) for an entire sequence.
## 3 Results
### Morphometric Measurements  
A total of 203 individuals of _C. sapidus_ were collected from the swamp from 2022 to 2024. Among the collected specimens, the majority were males, with only a smaller proportion being females. For each individual measurement of carapace width (CW), carapace length (CL), carapace depth (CD), and body mass were recorded. Carapace width varied between 7.13 cm and 15.40 cm, while carapace length ranged from 3.60 cm to 7.10 cm. Carapace depth ranged from 2.19 cm to 4.60 cm. Body mass of the specimens ranged from 34.00 g to 240.60 g (Table 1).  
Males were predominant in samples and made 82% of all collected crabs. They generally exhibited greater carapace width and mass compared to females, although some overlap in size was observed between the sexes. The largest individuals were primarily males, with the average carapace width recorded at 11.31 cm and the average body mass at 118.54 g. Females, while generally smaller, showed considerable variation, with the average carapace width of 10.36 cm and an average body mass of 77.64 g.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Count (n)** & **Average** & **Median** & **Min** & **Max** & **SD** \\ \hline Weight (g) & 203 & 111.08 & 100.00 & 34.00 & 240.60 & 45.79 \\ CW (cm) & 203 & 11.14 & 11.00 & 7.13 & 15.40 & 1.73 \\ CL (cm) & 203 & 5.30 & 5.26 & 3.60 & 7.10 & 0.68 \\ CD (cm) & 203 & 3.16 & 3.13 & 2.19 & 4.60 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Morphometric measurements and body mass of all collected _Callinectes sapidus_ individuals from the Palud-Palú swamp (count—number of collected individuals, SD—standard deviation).  
The results show the overall weight distribution of all collected crabs and most individuals weighed between 50 g and 150 g, with the highest frequency around 100 g (Figure 6).
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
However, implementing FL over wireless networks introduces additional complexities, including resource allocation, bandwidth limitations, fluctuating channel conditions, and energy constraints. With the rapid evolution of wireless technologies driven by 5G and the anticipated shift to 6G, new architectures are emerging to help solve the issues in deploying FL over wireless networks [25]. For instance, nonorthogonal multiple access (NOMA) can be leveraged in both the power domain [26] and code domain [27]. In the spatial domain, advanced techniques like massive multiple-input multiple-output (MIMO) [28], reconfigurable intelligent surface (RIS) [29], and unmanned aerial vehicles (UAVs) [30] can be integrated further to enhance the performance and efficiency of wireless networks. These advancements serve as the primary motivation for our survey, which aims to provide a comprehensive review of cutting-edge wireless network architecture designs for energy-efficient FL. From this point of view, our survey highlights critical research challenges,explores promising solutions, and outlines the future vision for efficient deployment of FL over wireless networks. The main contributions of this paper can be summarized as follows:  
* First, we provide a brief overview of the fundamental principles behind deploying FL in general wireless networks. Then, we introduce a resource allocation optimization problem for minimizing overall energy consumption composed of computation and communication energy consumption. * Next, we present a comprehensive survey of the latest wireless network architectures with energy-efficient designs for FL. These architectures are systematically categorized based on the distinct characteristics of the FL process, including centralized federated learning (CFL) and decentralized federated learning (DFL), offering a clear understanding of their respective strengths and use cases. * To enhance energy efficiency in deploying FL over wireless networks, this paper provides an overview of emerging advanced technologies that can be integrated into wireless architectures to boost system performance. Technologies such as SWIPT, IRS, and NOMA are explored for their significant potential to address the challenges of implementing FL in wireless environments effectively. * Finally, we discuss the challenges, opportunities, and potential research directions, considering emerging advanced technologies and future wireless network architectures.
## 4 Discussion
### Advantages and Limitations of Present Research and Future Work
Apart from the applied methods, the performance of ensemble learning generally varies depending on both the generation of input data from the whole sample and the integration of various learners for the final output. At present, many studies focus on the ensemble generation approaches for inputs of different learners, mainly including bagging [71], boosting [72], and random forests [73]. These algorithms can be combined with machine learning methods for producing more accurate solution than an individual method, which has been proved by previous studies both in theory and practice [74; 75]. For example, for the water quality forecasting, [PERSON] et al. [76] reported that a boosting algorithm in conjunction with wavelet-based ELM and wavelet-based ANFIS models performed better than their respective single models without using the ensemble technique, respectively. [PERSON] et al. [77] found the variability in predictive capability among nine different machine learning approaches for estimating the species distributions in three forest stands and obtained a better predictive results through multiple model ensemble technique. To our best knowledge, the suitability and ability of ensemble learning techniques have never been explored for carbon flux forecasting research. Therefore, it may be effective to enhance the performance of our applied models in terms of stability and predictability for the present study. However, it is beyond the scope of the current investigation and will be undertaken in our follow-up work.
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 2 Synthetic Methods
### Coprecipitation Techniques
#### 2.1.1 Synthesis of Magnetic Core/Silica Shell/Plasmonic Nanoparticles  
Silica is a common choice for coating the magnetic nanoparticles as it is biocompatible, can be functionalised with a variety of surface functional groups and aids internalisation of the nanoparticles into cells [10]. Silica can act as in important mediator layer between an inner magnetic core (e.g., Fe\({}_{3}\)O\({}_{4}\)) and an outer plasmonic layer (e.g., gold)--the silica allows the seeding of small gold nanoparticles onto its surface, thus allowing for the further reduction of a gold layer, negating the difficulty of reducing gold directly onto magnetite which is difficult due to the mismatch between the crystal lattices of the two materials [11].  
A range of Fe\({}_{3}\)O\({}_{4}\)@Ag magnetic plasmonic materials can be synthesised using an intermediate silica layer. Such Ag-coated magnetic nanoparticles can be produced through addition of a silica layer using a Stober synthesis. The silica provides a basis for further functionalisation, for example, in a number of cases, APTES ((3-aminopropyl))triethoxysilane) has been used to endow the substrate with amino groups which help facilitate the attachment of the plasmonic metal nanoparticles [19], in this case through sonication of the Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@APTES nanocomposites with AgNO\({}_{3}\)--this ensured the attachment of the AgNO\({}_{3}\) to the surface of the nanoparticles. Following this, the pH was re-adjusted to 11 and an outer silver shell was obtained by reduction of the nitrate with NaBH\({}_{4}\). A similar approach using Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@APTES was used to obtain a gold coated nanocomposite--in this case, however, the functional nanoparticles were dissolved in phosphate buffer to increase the positive charge; thus driving the electrostatic adsorption of the gold nanoparticles onto the surface of the magnetite core nanoparticles [20]. The raspberry-structured resultant Fe\({}_{3}\)O\({}_{4}\)@Au nanoparticles were loaded with curcumin and were then coated with polyethylene glycol (PEG), which acts as a cloaking agent to increase blood circulation time (Figure 1).
## 3 Results and Discussion
### Chemical Activation Process Test Results
#### 3.2.3 Pore Structure, XRD, and SEM Analysis
To summarize the findings, the optimization of chemical excitant, Ca(OH)\({}_{2}\), which is the hydration product of CaO and CaCO\({}_{3}\), increased the alkalinity of the volcanic rock powder (pH = 12.5-13.5) [61] and promoted the generation of hydration products such as calcium silicate (C-S-H) and calcium hydroaluminate (C-A-H), which in turn improved the mechanical strength of the volcanic rock powder; CaSO\({}_{4}\cdot\)2H\({}_{2}\)O acted as a coagulation conditioning agent, which further promoted the formation of C-S-H and calcium alumina formation, thus enhancing the crack resistance and durability of the material [62]. The respective mechanisms of action of CaO, CaCO\({}_{3}\), and CaSO\({}_{4}\cdot\)2H\({}_{2}\)O provided a reliable basis for optimizing the formulation of the excitant and enhancing the specific pathways. Although the above conclusions were consistent with related studies [63; 64], the compatibility problem of a chemical excitant with different types of volcanic ash may have led to different reaction efficiencies, and in the analysis of variance and extreme deviation of orthogonal tests, the inflection point of the influence of the CaO factor on the compressive strength was not prominent, and the significance was the weakest in the whole age period, so there is still room for the optimization and upgrading of this factor.
## 1 Introduction  
Nanotechnology has developed a reputation in nanoscience during the past twenty years. Researchers and scientists are actively working to create diverse nanoparticles from innovative materials for various uses [1]. One of the most significant transition metals for biological purposes is iron oxide. Hematite (\(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)) is the most stable polymorph of iron oxide and is ideal for biomedical applications due to its adaptable magnetic and optical characteristics, great chemical stability, and biocompatibility. There are a number of ways to make iron oxide nanoparticles, including mechanochemical methods, wire electrical explosion, electrochemical methods, and green synthesis.  
Plants are more favoured than microorganisms as biological resources because of their low cost and simple accessibility in nature. In addition to lowering the metal salt, the biomolecules used in the synthesis also functionalize the nanoparticles' surfaces, leading to synergistic benefits in applications such as cancer therapy or the development of antimicrobial agents [2]. A promising alternative method for creating nanoparticles is through green synthesis. Compared to the currently available, traditional physical and chemical approaches, green synthesis provides a number of benefits [3]. Iron oxides are one of the most biocompatible metal oxide nanoparticles due to their exceptional minuscule physical properties, including superparamagnetism, firmness in liquid solutions, low susceptibility to oxidation, long blood half-lives, and flexible surface chemistry. They have a wide range of applications in environmental regulation, including as antibacterial drugs, in the adsorption of dyes, in food-related processes, and in the biomedical sector (drug delivery, magnetic cells, immunoassays, and hyperthermia treatment of cancer) [4; 5; 6; 7; 8; 9; 10; 11]. In the current work, anhydrous ferric chloride was converted into \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles using a _Capparis zeyalancia_ leaf extract and the nanoparticles were examined for antimicrobial and antiproliferative activities.
# Pre-Training Multi-Concept Question Embeddings for Knowledge Tracing
## 5 Experiments
### Performance  
We selected several classic models in the knowledge tracing task to validate the effectiveness of our question embeddings, including the classic deep knowledge tracing model DKT and the Dynamic Key-Value Memory Network (DKVMN), replacing their question representation components with the outputs of our pre-trained model. We also compared our method with previous pre-training approaches like PEBG and BiCo. Through these comparisons across different models, we aim to comprehensively assess the performance improvement brought by our designed pre-trained question-embedding module in the knowledge tracing task.  
Students' response sequences are recorded as either correct (1) or incorrect (0). Therefore, we formulate the knowledge tracing task as a binary classification problem. Based on this, we utilize Accuracy (ACC) and the Area Under the Receiver Operating Characteristic Curve (AUC) as our evaluation metrics. ACC measures the proportion of correctly predicted responses among all responses, providing a straightforward assessment of overall model performance. AUC, on the other hand, evaluates the model's ability to distinguish between correct and incorrect responses by computing the area under the ROC curve,which plots the true positive rate against the false positive rate. A higher AUC value indicates the better discriminative capability of the model.  
The experimental results, as shown in Table 2, demonstrate that the models augmented with PMQE exhibit superior performance across all metrics. On the Assist09 dataset, the PMQE+DKVMN model achieved the highest accuracy (ACC) of 77.74% and an AUC value of 78.86%, representing improvements of 5.56% and 4.25% over the baseline DKVMN model, respectively. Similarly, on the Junyi dataset, the PMQE+DKVMN model outperformed other combinations, achieving an accuracy of 73.52% and an AUC of 75.81%, which are 2.92% and 4.01% higher than the DKVMN baseline, respectively. Furthermore, models utilizing PMQE outperformed previous pre-training approaches.  
The improvements in various performance metrics indicate that PMQE is an effective method for question representation, significantly enhancing predictive capabilities in knowledge tracing tasks and providing new insights and tools for educational data mining.
## 4 Theoretical Error Analysis
### Projection Accuracy  
As described in the localization phase, the simple geometric relationship Equation (6) is employed to project the virtual location of detector sensors from deep water to the surface. The errors in the projection process can be estimated by the error propagation formula and are bounded by:  
\[\Delta r\leq\frac{\partial r}{\partial v}\Delta v+\frac{\partial r}{\partial T _{R}}\Delta T_{R}+\frac{\partial r}{\partial T_{S}}\Delta T_{S}+\frac{\partial r }{\partial a}\Delta d \tag{11}\]where \(\Delta r,\Delta v,\Delta T_{S},\Delta T_{R},\Delta d\) are the errors in \(r,v,T_{S},T_{R}\) and \(d\), respectively. Based on the equation (11), it is implied that projection accuracy for localization in RLS scheme directly depends on the errors in propagation delay, sound speed and estimated depth by presser sensors. Here, we analyze and address each in detail.
## 3 Results
### Yg and the Contribution of Suboptimum SDT to Yg  
Modeled regional Ya and potential yield limited by suboptimal SDT (Yp0) and Yp in the period 2010 to 2015 are presented in Table 1 and Figure 7a. The ratio of Yg to Yp (Yg/Yp), the ratio of Ya to Yp (Ya/Yp), Yg caused by suboptimal SDT (Yg0), and the contribution of suboptimal SDT to Yg (CYg0) computed on the basis of Ya, Yp0, and Yp are also presented in Table 1 and Figure 7b. The spatial variations in annual mean Ya, Yg, Yg/Yp, Yp0, Yg0, and CYg0 are shown in Figure 8. Results show that large gaps (Ygs) remained between Ya and Yp on a regional scale or at a specific location over the NCP. Most areas of the study region had Yg values greater than 3 t \(\mathrm{hm}^{-2}\), and high Yp values were mainly distributed in the north and northeast (Figure 8b). Yg of approximately 99% of the study areas accounted for more than 30% of local Yp (Figure 8c). Annual regional Yg of summer maize in NCP ranged in 4.9-6.4 t \(\mathrm{hm}^{-2}\) with a mean value of 5.4 t \(\mathrm{hm}^{-2}\), accounting for approximately 45% of the mean annual regional Yp (Table 1 and Figure 8). As shown in Table 1 and Figure 7, considerable proportions of Yg were induced by suboptimum SDT. An estimated 80% of the study areas, Yg0 ranged from 1 to 4 t \(\mathrm{hm}^{-2}\) (Figure 8e). Yg0 also contributed to more than 20% of Yg in \(\sim\)85% of the study areas. Regional Yg0 in each year ranged from 1.4 to 2.2 t \(\mathrm{hm}^{-2}\), accounting for 29-42% of the annual regional Yg. The annual average of regional Yg0 contributes to 35% of annual averaged regional Yg in 2010-2015 (Table 1). The analyses above demonstrate that large Yg remained in summer maize croplands over the NCP, and the values of Yg varied in space and could be considerably reduced by optimizing the SDT.  
Figure 8: Maps of simulated mean annual Ya (**a**), Yg (**b**), the ratio of Yg to Yp (Yg/Yp) (**c**), potential yield limited by suboptimum SDT (Yp\({}_{0}\)) (**g**), Yg induced by suboptimum SDT(Yg\({}_{0}\)) (**h**), and the contribution of suboptimum SDT to Yg (CYg\({}_{0}\)) (**i**), with a 5 km resolution in 2010–2015; and the frequency distribution (FD) of pixel-level values of Ya (**d**), Yg (**e**), Yg/Yp (**f**), Yp\({}_{0}\) (**j**), Yg\({}_{0}\) (**k**), and CYg\({}_{0}\) (**l**). The value ranges in the legends are right-closed and left-open. Annual 1 km Ya, Yp\({}_{0}\), and Yp were aggregated to 5 km and then averaged in time to derive the mean annual Ya, Yp\({}_{0}\), and Yg and Yg/Yp were derived from the 5 km Ya and Yp, and Yg\({}_{0}\) and CYg\({}_{0}\) were derived from the 5 km Yp\({}_{0}\), Yg, and Yp. Each 5 km grid represents all 1 km cropland pixels inside the grid.