esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Micro Air Vehicles (MAVs) airfoils usually operate at low Reynolds number conditions, where viscous drag will consume a large amount of propulsion power. Due to the small dimensions, many drag reduction methods have failed, resulting in limited current research. To develop an effective method of reducing viscous drag, transverse grooves were placed on the surface of MAVs airfoils in this study, and a numerical investigation was implemented to uncover the corresponding flow control law as well as the mechanism. Research has shown that transverse grooves have an impact on the drag and lift of airfoils. For drag, properly sized transverse grooves have the effect of reducing drag, but under high adverse pressure gradients or when the continuous arrangement of grooves is excessive, the optimal drag reduction effect achieved by the grooves is weakened, and even the drag increases due to the significant increase in pressure difference. In severe cases, it may also cause strong flow separation, which is not conducive to MAV flight. For lift, the boundary vortex in the groove has the ability to reduce the static pressure near the groove. However, high adverse pressure gradients or too many grooves will thicken the boundary layer and increase the blockage effect, resulting in a large static pressure on the grooved side of the airfoil (with an increase in drag). From the perspective of circulation, the static pressure changes on the suction and pressure surfaces have opposite effects on lift. Considering the comprehensive aerodynamic performance of the airfoil, we designed a high lift-to-drag ratio airfoil with grooves, which increased the lift-to-drag ratio by 33.747% compared to the smooth airfoil. Based on the conclusions, we proposed preliminary design criteria for grooved airfoils, providing guidance for subsequent research and applications.  
MAV airfoil; low Reynolds number; transverse grooves; aerodynamic performance Article  
A Numerical Study on the Aerodynamic Performance of the Aerodynamic Performance of Micro Air Vehicles Airfoils [PERSON] et al. (2020), [PERSON], [PERSON], and [PERSON] (2020), and [PERSON] (2020)
# A Method for Evaluating Systematic Risk in Dams with Random Field Theory
## 3 Risk Analysis Method for Gravity Dams Considering the Spatial Variability of Parameters  
### Construction of Performance Functions for Strength and Stability Failure  
The rock foundation under a gravity dam has complex stress states, failure mechanisms, and failure paths. The performance functions of dam-foundation systematic failure should be constructed by using critical failure paths with a consideration of material prop erties, constitutive relations, and failure criteria. In this paper, the load increment method is adopted to search the critical failure paths of the gravity dam-foundation system.  
When conducting a safety risk analysis of gravity dams, strength failure and stability failure are the main two critical failure modes for gravity dams [31]. The yield failure of concrete and rock under stress and the sliding force of the foundation surface or deep sliding surface greater than the resistance force both belong to the ultimate bearing capacity limit state. Therefore, the strength failure of the dam body and the stability of the dam foundation can be expressed using corresponding strength criteria and shear resistance formulas in the finite element analysis.  
For brittle materials like rock and concrete, the Drucker-Prage criterion is commonly adopted to construct the performance function, \(G_{1}(X)\), of strength failure, that is  
\[G_{1}(X)=k-aI_{1}-\sqrt{J_{2}} \tag{7}\]  
\[I_{1}=\sigma_{1}+\sigma_{2}+\sigma_{3} \tag{8}\]  
\[J_{2}=\frac{1}{
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 3 Results
### Internal Friction Angle  
#### 3.3.1 Relationship Between Internal Friction Angle and Water Content  
Figure 7a illustrates the variation in the internal friction angle of fault gouge with water content under varying degrees of cementation. The overall trend indicates that with increasing water content, the internal friction angle exhibits a significant decrease, demonstrating a negative correlation between water content and internal friction angle [31]. This suggests that the addition of water forms a thin film between the particles, leading to a reduction in the internal friction angle. This effect is particularly significant for fault gouge samples with low degrees of cementation [41].  
In the case of a cementation degree of 0%, when the water content increases from 10% to 30%, the internal friction angle of the fault gouge significantly decreases from 33.06\({}^{\circ}\) to 9.87\({}^{\circ}\), a reduction of up to 70.1%. This result indicates that under conditions without cementing materials, the increase in water greatly reduces the friction between particles.  
For samples with a cementation degree of 3%, as the water content increases from 10% to 30%, the internal friction angle decreases from 31.42\({}^{\circ}\) to 29.94\({}^{\circ}\), a reduction of only 4.7%. This shows that the increase in cementing materials offsets the lubrication effect brought  
Figure 7: Internal friction angle relationship curves: (**a**) relationship between internal friction angle and water content; (**b**) relationship between internal friction angle and cementation degree.  
about by the additional water to some extent, allowing the internal friction angle to remain at a relatively high level.
# A Pilot-Pattern Based Algorithm for MIMO-OFDM Channel Estimation
###### Abstract  
An improved pilot pattern algorithm for facilitating the channel estimation in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems is proposed in this paper. The presented algorithm reconfigures the parameter in the least square (LS) algorithm, which belongs to the space-time block-coded (STBC) category for channel estimation in pilot-based MIMO-OFDM system. Simulation results show that the algorithm has better performance in contrast to the classical single symbol scheme. In contrast to the double symbols scheme, the proposed algorithm can achieve nearly the same performance with only half of the complexity of the double symbols scheme.  
MIMO; OFDM; channel estimation; least square; pilot 1
## 2 Materials and Methods
### Ion Chromatography Analysis  
Quantification of the main ionic components and evaluation of the presence of soluble salts were performed with ion chromatography (IC). Anions (NO3\({}^{-}\), SO4\({}^{2-}\), Cl\({}^{-}\)) and cations (Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\), NH4\({}^{+}\)) were determined using an HPLC Dionex ICS-1000 Ion Chromatography System (Thermo Scientific, Dionex Corporation, Sunnyvale, CA, USA) equipped with a conductivity detector. Anion analysis was carried out with an Ion Pac AS14A IC Column, using 8 mM Na2 CO3/1 mM NaHCO3 as the eluent, isocratic elution, a constant flux of 1.5 mL min\({}^{-1}\) and an ULTRA anionic self-healing suppression (ASRS-ULTRA), whereas cations were analyzed with an Ion Pac CS12A IC Column, using 20 mM methanesulfonic acid (MSA) as the eluent, isocratic elution, a constant flux of 1.5 mL min\({}^{-1}\) and an ULTRA cationic self-healing suppression (CSRS-ULTRA).  
In order to prepare the solutions to be analyzed, a small portion of the sample was ground in an agate mortar. Of this powder, around 2 mg was transferred in a plastic test-tube and treated with 10 mL of milli-q water (Merck Millipore Milli-Q, Burlington, MA, USA). The suspensions were immersed in an ultrasonic bath for 1 h, centrifuged at 3000 rpm for 3 min and filtered using 0.45 um non-sterile hydrophilic membranes (PTFE Millex-14 LCR, 25 mm, Millex(r) Syringe Filters, Merck Millipore, Burlington, MA, USA) before injection in the instrument.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 5 Simulation Results and Performance Evaluation
### Simulation Results
#### 5.2.3 Energy Consumption  
Figure 11a,b show energy consumption of existing and proposed schemes in scenario one and scenario two, respectively, with different \(DRs\) and \(PIs\). Energy consumption depends upon the \(DRs\) irrespective of the \(Pl\). We set \(DR\) to 10, 20 and 30 KB/s and \(Pl\) to 100, 200, 300, 400 and 500 bytes.  
High number of active nodes in LMPC as shown in Figure 9, increases collision in the dense network which results in packet drop. While, sparse regions effect PRR by dropping data packets in void region. In existing and proposed schemes, multiple copies are directed towards surface in order to eliminate retransmissions and save energy.  
Unlike LMPC, tree generation from source node, FLMPC-One and FLMPC-Two establish binary tree from cross nodes only which results in less number of duplicate data packets. FLMPC-One and FLMPC-Two reliably transmit data packets to most suitable nodes and save data packets from entering void region. These reliable and less number of data packets ultimately saves energy and increases PRR as shown in Figure 12.  
In scenario one, FLMPC-One saves almost 8-12.5% energy while, FLMPC-Two saves 23-26% energy of the network as shown in Figure 11a. FLMPC-One saves 8.33-12.7% energy and FLMPC-Two saves 23.8-29% energy in scenario two. FLMPC-One and FLMPC-Two reduces energy consumption at the cost of delay. In FLMPC-Two, every node performs calculations up to three hops in order to avoid void holes which increases PRR and saves energy. FLMPC-Two achieves this by paying cost of high delay as shown in Figure 10.  
In DBR, energy consumption is higher in both scenarios one and two as compared to LMPC and FLMPC schemes. The high energy consumption is because of void hole occurrence when forwarding data packets with greedy approach. Moreover, it can be seen from Figure 11a,b, that with the increase in packet size, the energy consumption also increases. The reason for more energy dissipation in DBR is because of inefficient approach to handle the void holes. The occurrence of void holes increase the packet drop ratio resulting in unwanted energy depletion. While in LMPC and FLMPC transmission of data packets from multiple paths and generation of data packets from cross nodes ensure that the packet will not be re-transmitted.
## 2 Materials and Methods
### Statistical Analysis  
The resulting data matrix was imported into MetaboAnalyst 5.0 online platform [22] and graphically displayed by using several R packages (\"ComplexHeatmap\" version 2.11.1, \"Circlize\" version 0.4.13, and \"ColorRamps\" version 2.3). The obtained data were normalized by sum. First, principal component analysis (PCA) was applied to provide an exploratory data analysis. PCA is effective for separating features into groups based on commonality and reports the weight of each component's contribution to the separation. The mechanism underlying PCA is an orthogonal transformation transferring a set of correlated variables into a new set of uncorrelated variables. PCA is a preliminary step in a multivariate analysis to provide an unsupervised overview of the samples. An unsupervised PCA analysis on MetaboAnalyst 5.0 was carried out to determine how metabolites differ from each other, and which compounds contribute the most to this difference. Moreover, a hierarchical cluster analysis (HCA) was performed to obtain a dendrogram of varieties, based on Euclidean distance, using the metabolome dataset. Lastly, the differences in the metabolites were detected using PLS-DA. The corresponding VIP values were calculated using the PLS-DA model. The VIP value represents the difference between the considered variables. A VIP value above 1.5 indicated components that play an important role in differentiating between samples. Only components with VIP \(>\) 1.5 and \(p\) \(<\) 0.05 were selected as potential markers. Additionally, a heatmap was plotted to visualize the variations in potential markers for separating samples with processing times into different groups. To measure the linear correlation between datasets, a correlation matrix based on Pearson Correlation Coefficient was performed by using MetaboAnalyst 5.0. The result has a value between \(-\)1 (strong inverse correlation) and 1 (strong direct correlation).
## 3 Conceptual Model: Procurement Order Quantities Regulation for Shortage Scenarios
### Target System: Key Performance Indicators  
Some major reasons for organizations to undertake activities are to save money or increase profit, or influence other important factors, such as quality, delivery time, or delivery reliability, which are essential for competitiveness [1]. The results will be quantitative to evaluate the responses according to the following performance indicators, and the detailed mathematical formulation can be found in Appendix B:  
1. Gross demand/needs (final product units): The sum of the final product demand, and therefore represents the gross needs as an input parameter of the simulation model;
2. Service level (% over quantity): The percentage of products produced on time according to planning;
3. Service level (% on days): The percentage of the days in which the production manufactured the required quantity. It is declared at the end of the production process and is considered as a demand not satisfied on time in those cases in which the quantity sent is less than the demand of the clients on the current day plus the delays of the previous days;
4. Delays (product units): The quantity of products delivered late to the customer according to planning;
5. Delays (days): The days in which the customer deliveries did not reach the required quantity;
6. Average stock level (units of materials/products): The average value of units in the inventory;
7. Number of orders (number of orders): The number of orders placed during the simulation time horizon;
8. Total inventory costs (USD): The sum of the procurement costs from an external supplier, the warehousing costs, the materials planning, and the handling costs;
9. Procurement management costs (USD): The total procurement cost minus the cost of the parts or materials from which it is procured. It is the value of planning, ordering, and handling management;
10. Procurement costs (USD): In the model, this is applied as external procurement costs and not as in-house production. Therefore, the external procurement costs depend on the order quantity and cost price as direct costs and the order costs and cost rate of the order initiations [32];
* Warehouse storage costs (USD): The sum of capital commitment costs and storage costs [32];
* Capital commitment costs (USD): The function of the interest rate, inventory quantity, and its inventory value and storage time;
* Storage costs (USD): The components of shortage costs are lost contribution margins, reduced revenues, and additional costs, such as contractual penalties [32]. For the model, it is considered as a penalty per unit not delivered on time in each period;
* Stock-out costs (USD): The model considers a fine for each unit of product not delivered on time for each period of delay in delivery.
## 3 Precise Orbit Determination Strategy
### Processing Procedure  
For each three-day solution, the processing procedure is illustrated in Figure 4. First of all, data pre-processing is carried out station by station to remove outliers and to flag cycle slips. Then, an initial orbit is generated by orbit integration. With the initial orbits and pre-processed observations least-squares adjustment is performed to estimate the parameters. Data editing based on post-fit residuals is undertaken to detect any possibly problematic observations. The last three steps must be run iteratively to obtain a free solution until the solution is converged with no more cycle slips and outliers are detected. Afterwards, ambiguity fixing can be carried out to obtain the fixed solution. After  
\begin{table}
\begin{tabular}{l l} \hline
**Item** & **Models** \\ \hline Observations & Undifferenced ionosphere-free code and phase combination of B1 \\  & and B2 with 60 seconds sampling \\ Elevation cutoff & 7 \({}^{\circ}\) \\ Weight for observations & Elevation dependent weight \\ Phase-windup effect & Applied [16] \\ Earth rotation parameter & Estimated with tight constraint \\ Tropospheric delay & Initial model \(+\) random-walk process \\ Ionospheric delay & Eliminated by ionosphere free combination \\ Satellite and receiver clock & White noise \\ Station displacement & Solid Earth tide, pole tide, ocean tide loading \\  & IERS Convention 2003 [17] \\ Satellite antenna PCO and PCV & Not corrected \\ Receiver antenna PCO and PCV & Not corrected \\ \hline \end{tabular}
\end{table}
Table 2: Summary of observation models and parameters applied in POD.  
\begin{table}
\begin{tabular}{l l} \hline
**Item** & **Models** \\ \hline Geopotential & EGM96 model (12 \(\times\) 12) \\ Tide & Solid Earth tide, pole tide, ocean tide \\  & IERS Conventions 2003 \\ M-body gravity & Sun, Moon and all planets (JPL DE405) \\ Solar Radiation Pressure & Reduced BERN five parameters with no initial value \\ Relativistic Effect & Applied \\ Velocity breaks & Every other 12 hours \\ Attitude model & Assuming the same as GPS satellite of Block IIR \\ \hline \end{tabular}
\end{table}
Table 3: Dynamic models involved for BeiDou orbit determination.  
each adjustment, estimates including satellite orbits, station coordinates, and clocks of both stations and satellites should be updated for the next iteration or as final results.  
It should be mentioned that for the newly launched MEOs M3 and M4, there were no broadcast navigation information. Their initial orbit conditions are estimated from BeiDou range observations with receiver clock and station coordinates derived from GPS.
## 3 Bibliometric Analysis  
### Research Performance  
As shown in Figure 2, research on urban HErC in China began to gradually increase from 2009, reaching 18 publications in 2022. Around 2015, a key milestone in this field occurred with the Paris Climate Conference, where the Paris Agreement significantly influenced national energy policies, leading to a rapid rise in related publications. Papers published in 2024 are relatively recent, with the search cutoff in June 2024, showing some delay. Given that the included literature underwent strict manual screening, some articles with similar topics but not meeting the research object were excluded, which explains the absence of a consistent upward trend. However, this does not affect the overall analysis, as the number of articles on this topic from 2000 to 2024 shows a linear growth trend (R\({}^{2}\) = 0.780). This also indicates that the household sector, as a significant source of carbon emissions, is receiving increasing attention regarding its emission reduction potential and measures.  
Figure 1: The process of literature handling.  
In terms of disciplinary focus, the research spans 24 distinct fields, as identified by the WOS platform. Studies on HErC are inherently interdisciplinary, reflecting the complexity and multifaceted nature of the topic. As shown in Figure 3, the 98 target articles are associated with 207 occurrences across disciplines, indicating that each study involves an average of 2.15 disciplines. \"Environmental Sciences\" ranks first, with over half of the articles linked to this field, highlighting significant attention to the environmental impacts of HErC. \"Energy Fuels\" and \"Green Sustainable Science Technology\" follow with 34 and 27 occurrences, respectively, emphasizing the importance of sustainable energy practices and green technology innovation in the household sector. Other notable fields include \"Environmental Studies\" (19), \"Engineering Environmental\" (16), \"Economics\" (11), and \"Thermodynamics\" (9). These findings underscore the value of research on HErC in advancing knowledge across environmental, economic, and sustainability domains.  
Over the past two decades, research on urban HErC in China has been published in 38 different journals, as shown in Table 1. Based on total publications, total citations, and average citations, the top ten contributing journals are identified, along with their impact  
Figure 3: The disciplinary focus.  
Figure 2: The number of publications.  
factors. In terms of publication volume, \"Journal of Cleaner Production\" is the leading journal with 15 articles, followed by \"Energy\" with 9 articles. Both of these journals also rank highly in total citations, with 548 and 225 citations, respectively. Average citations serve as an important measure of academic impact, with \"Applied Energy\" leading at 91.5, followed by \"Energy Policy\" and \"Energy Economics\". This indicates that papers published in these three journals are widely cited by researchers. Notably, these three journals also have the highest impact factors, reflecting their significant influence and contribution to the field.
# A Graph Convolution Collaborative Filtering Integrating Social Relations Recommendation Method
## 2 Related Work  
### Traditional CF Recommendation Algorithm  
In e-commerce industry, collaborative filtering (in short, CF) has been widely used, and in the past two decades many CF algorithms have emerged in academia and industry. Roughly speaking, CF algorithms is classified into two categories: neighborhood-based CF algorithms [4] and model-based recommendation algorithms.  
Neighbor-based CF algorithms [5; 6; 7; 8] can find potentially relevant items from user past behaviors without any domain knowledge, and they can be further classified into two specific types which are user-based CF and item-based CF. The key of neighborhood-based CF methods is how to calculate similarity and sum up these scores.  
The primary idea behind the model-based CF algorithms [9] is to embed users as well as items into the same embedding space, and then make prediction through the inner-product of their embeddings. Using data mining and machine learning techniques, model-based approaches predict unknown scores by finding patterns in training data.  
However, the accuracy of traditional CF recommendation method is limited because they make only use of interactions between users and items to predict unknown ratings.