esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Algorithm 2 was applied to solve the sowing machine problem, so as to select the parameters yielding the optimal seeding accuracy of the air-assisted rice seeder.  
_Step 1_: The data table was normalized according to each criterion to obtain a fuzzy decision matrix.  
\(+\): Air-assisted velocity  
\[y_{C_{1}}(x)=\left\{\begin{array}{ll}\frac{x-3\overline{5}}{15}\ \text{if}\ 3.5\leq x\leq 5\\ \frac{x-3}{2}\ \text{if}\ 5\leq x\leq 7\\ 0\ \text{if}\ \textit{others}\end{array}\right.\]  
\(+\): Internal diameter of injection port  
\[y_{C_{2}}(x)=\left\{\begin{array}{ll}\frac{x-10}{5}\ \text{if}\ 10\leq x\leq 15\\ \frac{20-x}{5}\ \text{if}\ 5\leq x\leq 20\\ 0\ \text{if}\ \textit{others}\end{array}\right.\]  
\(+\): Distant between injection port and soil surface  
\[y_{C_{3}}(x)=\left\{\begin{array}{ll}\frac{x-70}{80}\ \text{if}\ 70\leq x\leq 150\\ 1\ \text{if}\ 150\leq x\leq 200\\ \frac{250-x}{50}\ \text{if}\ 200\leq x\leq 250\\ 0\ \ \text{if}\ \textit{others}\end{array}\right.\]  
\(+\): Seeding area precision (diameter 50 mm)  
\[y_{C_{4}}(x)=\left\{\begin{array}{ll}0\ \text{if}\ 0\leq x\leq 60\\ \frac{x-60}{20}\ \text{if}\ 60\leq x\leq 80\\ 1\ \text{if}\ 80\leq x\leq 100\end{array}\right.\]  
\(+\): Seeding area precision (diameter 100 mm)  
\[y_{C_{5}}(x)=\frac{100-x}{100}\]  
\(+\): Seeding area precision (diameter 150 mm)  
\[y_{C_{6}}(x)=\frac{100-x}{100}\]\[+\quad\text{Seeding depth}\] \[y_{C_{7}}(x)=\left\{\begin{array}{ll}\frac{x-3}{2}\text{if }3\leq x \leq 5\\ 1\text{if }5\leq x\leq 10\\ \frac{15-x}{5}\text{if }10\leq x\leq 15\\ 0\text{if others}\end{array}\right.\]  
Then, the normalized data shown in Table A1 (Appendix A) were obtained.  
_Step 2._ The weight vector \(\mathbf{w}=(0.025,\,0.025,\,0.025,\,0.2,\,0.025,\,0.6)\) was chosen. Due to the requirements of the problem, the weight determined in the criterion of seeding depth was greatest to meet the criterion of accuracy within 50 mm.  
_Step 3:_ The best option and the worst option were determined, as shown in Appendix A (Table A1).  
_Step 4._ Equation (2) was used to compute the similarity measures \(S_{i}^{+}\) and \(S_{i}^{-}\) for each alternative \(P_{i}\) (\(i=1,2,\ldots,m\)) to identify \(P^{+}\) and \(P^{-}\), respectively. The results are shown in Table A2 (Appendix B).  
_Step 5._ The close coefficient \(CC_{i}=\frac{S_{i}^{+}}{S_{i}^{+}+S_{i}^{i}}\) (\(i=1,2,\ldots,m\)) was determined as shown in Table A2 (Appendix B) and Figure 2.  
_Step 6._ Accordingly, the top five results could be ranked as \(E8\succ E4\succ E19\succ E12\succ E22\).
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 3 DPTracer Design
### DPN Model
#### 3.5.5 Hierarchical Data Delivery  
We define a hierarchical structure called the 'Report Tree', through which nodes deliver data during data provisioning. As depicted in Figure 2, the submitter sits at the root of the Report Tree, with collector nodes positioned as the leaves and aggregator nodes located in between and aggregating multiple inputs into a single output. The construction of the Report Tree can be automatically performed each round, as in WSN [21], based on the predefined membership for the round. The logger and the submitter are informed of the Report Tree's structure at the beginning of each round. In smaller DPNs, the submitter may function as the sole aggregator node and directly aggregates results from all collector nodes to produce the final output. Each node sends its output (_upstream report_) to its parent node in the Report Tree. For instance, in round \(r\), an upstream report produced by node \(n_{\alpha}\) is labeled as \(R^{r}_{\alpha}\). (For brevity, we omit the round number \(r\) in \(R\) for the current ongoing round.) When two collector nodes \(n_{\alpha}\) and \(n_{\beta}\) generate \(R_{\alpha}\) and \(R_{\beta}\), respectively, from external data sources, an aggregator node \(n_{\gamma}\) receiving these will combine them into \(R_{\gamma}\): \(R_{\gamma}=R_{\alpha}\sqcap R_{\beta}\), where \(\sqcap\) denotes the aggregation operator.
## 4 Applications
### Potential Industrial Applications  
Besides motion monitoring and disease diagnosis, the strain sensor can also be used in a broad range of fields, including automotive, power systems, and robotics, while aiming to create applications in solution services targeting the Internet of Things. For example, amount of strain sensors employed in automotive devices and robots can accurately measure strain changes caused by forces exerted on those devices [39; 40]. In this way, it will be possible to efficiently manage and control status of those, to enable the smooth operation of devices, and to diagnose the signs of potential failures. Compared with other exist strain sensors, our fabric-based strain sensor possesses the following two advantages. First, the softness of the fabric strain sensor makes it conveniently conform to the curvilinear surfaces of complex devices. Second, the fabric-based strain sensor due to TsiPD bonding has much better durability.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 2 Inorganic Stabilization Materials
### Nano-Metal Oxides  
Nanometal oxides are referred to as metal oxide materials with particle sizes ranging from 1 to 100 nm. They include TiO\({}_{2}\), ZnO, Fe\({}_{2}\)O\({}_{3}\), CeO\({}_{2}\), etc. The specific surface area of nanometal oxides is usually considered to be very large. It is noted that the large specific surface area not only provides more reaction sites but also enhances its adsorption capacity.  
Figure 4: (**a**) XRD before and after Fe-HAP remediation of As and other pollutants [37]; (**b**) The occurrence form of As before and after Fe-HAP repair [37]; (**c**) Pseudo-second-order dynamic fitting of Fe-HAP to As [37]; (**d**) The occurrence forms of As in soil after correction with different materials [38]; (**e**) The effect of the combined use of magnesium ascorbate phosphate (MAP) and phytase on the removal rate of Cr(VI) [39].  
[PERSON] et al. [40] mechanically activated limonite by a ball mill. After mechanical activation, the specific surface area of limonite increases. Compared to untreated brown iron ore, the adsorption rates of As(V) and As(III) have increased by 12.23 and 2.99 times, respectively (Figure 5a). Due to the formation of a relatively stable inner-ball complex, the addition of 10% ball milled limonite increased the residual state F5 of As in the soil by 16.25% (Figure 5b).  
Nano metal oxide dual functional materials have been developed, which combine photocatalytic performance and adsorption function and have received high attention. The Fe\({}_{3}\)O\({}_{4}\) magnetic core prepared by [PERSON] et al. [41] was encapsulated by a dual functional p-n heterojunction constructed by FeOOH modified carbon-doped TiO\({}_{2}\) (FeOOH@C/TiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\)), which is spherical in shape (Figure 5c). Under visible light irradiation, the carbon doping of TiO\({}_{2}\) was found to reduce the bandwidth, promote the separation of photo-generated electrons and holes, and generate active substances (such as -OH and -O\({}_{2}\)\({}^{-}\)) that could oxidize As(III) to As(V). The adsorption process conforms to chemical adsorption. Constructing a material model, the adsorption energy was calculated to be only \(-\)1.9732 eV (Figure 5d). Nickel-modified zinc oxide (ZnO-Ni) was synthesized by [PERSON] et al. [42]. Within 150 min, 22% of Cr(VI) was removed under illumination via zinc oxide. Under visible light irradiation, it was found that nickel oxygen clusters promoted the transfer of interfacial charges from the valence band of ZnO to nickel. The photo-generated electrons could be used to directly reduce Cr(VI) to trivalent chromium (Cr(III)) (Figure 5e). [PERSON] et al. [43] also found that photo-generated electrons play a major role in the reduction of Cr(VI).
# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 5 Description of AI Toolbox/Breast Cancer Tools
### Federated Learning--Data Exploitation
#### 5.1.2 Hybrid Infrastructure and Federated Learning  
Federated learning enables training without having the data directly accessible. This is positive for data privacy; however, it works in detriment of ease of use. For this reason, in the INCISIVE project, a hybrid infrastructure is planned so that data can be shared in a central repository. In particular, this is offered from the start in a Cloud-based infrastructure (temporary infrastructure) so that the AI developers from the project can start their work as soon as possible. This way, the developers and researchers can test their code and check the cases in which their models fail.  
With this hybrid infrastructure, INCISIVE will be able to also offer storage for those data providers that do not have an infrastructure to hold the federated learning components. This makes the infrastructure accessible and versatile.
## 3 Results and Discussion
### Effect of Diverse Ions  
Flame atomic absorption spectrometry (FAAS) provided high selectivity towards analyte determination. Interferences from other cations may be attributed to the pre-concentration step, in which these metal ions can form stable complexes when they react with the ligand. This could lead to a decrease in extraction efficiency. To study the effect of interfering ions, different amounts of foreign ions were added to the standard solution of 10 ug/L of Pb\({}^{2+}\) and the recommended procedure was followed. The recoveries of Pb\({}^{2+}\) ions in these studies were higher than 95%. The tolerable limit was defined as the largest amount of foreign ions that produced an error not exceeding \(\pm\)5% in the determination of Pb\({}^{2+}\). The results were presented in Table 1. From the results obtained, we can conclude that the existence of high amounts of interfering ions that can be commonly present in water samples have no remarkable effect on the CPE of Pb\({}^{2+}\) ions.  
Figure 4: Effect of Triton X-114 concentration on the CPE-efficiency. Conditions: 40 ug/L Pb(II); pH 6; DLNL \(2\times 10^{-5}\) M.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 1 Introduction
After the innovative work on the synthesis of MIPs using silica matrices by [PERSON] in the 1930s [14], MIT has grown to become as a broad area of interest for the scientific community in general. Numerous reviews [15; 16; 17; 18; 19; 20], articles [21; 22; 23; 24], and books [25; 26; 27] have been published on the topic of molecular imprinting. This number is increasing continuously which shows the trends and developments in the area of MIP. Sensors based on the molecular imprinting technique are one of the most interesting topics discussed nowadays due to the high specificity and stability of MIPs in harsh environments. Using SPR and fiber optic configuration, the sensing ability of the MIPs is greatly enhanced. In this review we have summarized the combined approaches of the SPR/LSPR phenomenon with molecular imprinting in fiber optic configuration. In the present article, the research work carried out on optical fiber sensors utilizing SPR and MIT for the detection of various chemical and biological analytes is reviewed. The review, which starts with a discussion on surface plasmon resonance explains the conditions required to bring about the phenomenon, various interrogation schemes to analyse the response in sensing in terms of intensity, phase, polarization and wavelength. Fiber optic-surface plasmon resonance (FO-SPR) implementation is discussed with the parameters which qualify the sensor performance followed by the possibilities of metal nanostructures in place of metallic film to generate localized surface plasmons. Further, the review covers the molecular imprinting technology with a brief introduction, synthesis procedures of molecularly imprinted polymers (MIPs), and the role of each element used for MIP preparation. The sensing mechanism using FO-SPR- and MIP-based sensors and the studies reported on this topic are also discussed in this review. To conclude, the advantages and the future challenges for improving the sensor performance are discussed.
# Identifying and Ranking Influential Nodes in Complex Networks Based on Dynamic Node Strength
## 2 Methods
To illustrate the procedure of DNSD in detail, an example is shown in Figure 2, in which \(\alpha\) in Equation (2) is set to 0.5 and \(\beta\) in Equation (3) is set to 0 simply. To further improve the accuracy of DNSD, the \(S\) of the target node and its neighbors are also considered. This is based on the idea that crucial nodes not only are located at crucial locations in the network but also have more edges with other nodes located at crucial locations [24; 32]. In this way, the improved node strength is defined as:  
\[S_{i}^{+}=S_{i}+\sum_{j\in\gamma(i)}S_{j}, \tag{4}\]  
where \(\gamma(i)\) is the set of neighbors of node \(i\). The improved method is called DNSD\({}^{+}\). We simply compare the effect of our methods with several methods in the sample network of Figure 1. The results are shown in Table 1. It can be seen there are many nodes with the same rank in the first three methods, among which the k-shell method simply divides the nodes into three ranks. Obviously, DNSD and DNSD\({}^{+}\) can distinguish nodes more precisely, even though there are still several ranks greater than 12 that are not displayed.
## 5 Performance Evaluation
### Performance Evaluation of ICN Hierarchical Cache System
#### 5.1.3 Performance Comparison in Real-World ISP Topology  
The TICCALI topology has 240 nodes and 810 edges, including 36 content sources, 160 cache-supported routing nodes, and 44 receivers. Three kinds of cache systems were evaluated in different caching strategies (LCE, LCD, CL4M, and LCP with \(p=0.2\)). In detail, the first is a HCS based on a hierarchical NRS; the second is a flat cache system (FCS) with a single resolution node (which is a special case of a hierarchical caching system with the single level); and the last is a CCN-like [23] cache system based on routing requests by name.  
Figure 7 reports the access latency comparison of different cache systems with different source selection mechanisms (nearest and random) under the workload of YouTube traces from the campus network. With the increase of the routers' cache size, selecting the nearest content copy in the HCS (HCS-N) significantly reduced the download latency among all the caching strategies by up to 18.6% compared with the cache system based on routing requests by name. This proves that, while routing requests by name avoids resolution latency, it is difficult to find an optimal content duplicate. Moreover, the HCS performed better than the FCS because the HCS took advantage of the characteristic of hierarchical content addressing, in which the distance information between users and content is hidden  
Figure 6: Hit ratio vs cache size for various caching strategies in different approximation approaches. LCD: leave copy down; LCP: leave copy with probability; and LCE: leave copy elsewhere.  
in the hierarchical structure. In other words, the HCS achieved a more efficient content discovery at the cost of a tolerable resolution latency.  
Randomly selecting the content copy in the HCS (HCS-R) achieved the second best performance of access latency, similar to the HCS-N under LCE and LCP. This was because both caching strategies only needed single request to fetch the cache content in the L1 HEA that was closest to user. Contrary to the above strategies, LCD and CL4M only cached one or a few content copies on the transmission path each time, which means that users could not retrieve content in the L1 HEA the first time. This conclusion also confirms our analysis regarding the source selection mechanism in Section 3.2.  
As for the cache hit ratio, Figure 8 illustrates that the mechanism of choosing the nearest content copy achieved the highest cache hit rate, which was irrelevant to the structure of a cache system and caching strategies. The HCS-R still achieved a suboptimal performance because of the aforementioned analysis. The performance of the cache systems of routing requests by name in LCD outperformed the other strategies since it was an implicit cooperative caching strategy. It should be noted that LCP achieved a significantly better performance than other strategies because it better avoided the simultaneous placement of the content in all routers.
## 3 Theoretical Models and Verifications  
### Aerodynamic Model  
In this work, the combination of blade element theory and momentum theory is used to estimate the aerodynamic performance and calculate the AEP. The blade element theory aims to calculate the aerodynamic force acting on each blade element. The blade momentum theory refers to analysis of both axial and tangential induced velocity by introducing axial and angular induction factors. The induced velocity in each blade section influences the angle of attack of the airfoil and therefore affects the loads acting on the blade sections. With an iterative procedure, BEM theory provides a method to calculate the aerodynamic loads on each blade element. For a rotor with a finite number of blades, the vortex system in the wake is different from that of a rotor with an infinite number of blades [19]. By introducing the Prandtl's tip loss factor, the BEM theory can simulate a wind turbine rotor with a finite blade number.  
The aerodynamic performance of the total wind turbine blade can be obtained from each blade section. Thus, it is necessary to acquire the lift and drag coefficient of the adopted airfoil. However, the experimental polar of airfoils are not often available. In this work, RFoil [20] is used to estimate the aerodynamic characteristics of the airfoil. The RFoil code is an improved version of XFoil [21] and has many advantages such as better corrections after stall.  
The annual average wind speed is the main source of wind power. Due to the friction of the plants and constructions, better wind resources are located at higher heights. The friction coefficient \(n\) could affect the average wind speed at hub height, and the different average wind speed at hub height could have an impact on the AEP of wind turbines. In this work, the friction coefficient is set as 0.23 which corresponds to the roughness of the countryside. The wind speed is varied depending on the hub height according to the following equation:  
\[V_{\text{{hub}}}=V_{\text{{ref}}}\Bigg{(}\frac{H_{\text{{hub}}}}{H_{\text{{ ref}}}}\Bigg{)}^{n} \tag{2}\]  
where \(V_{\text{{hub}}}\) is the wind speed at the hub; \(H_{\text{{hub}}}\) is the hub height; \(V_{\text{{ref}}}\) is the reference wind speed at the reference height; \(H_{\text{{ref}}}\) is the reference height; and \(n\) is the friction coefficient and set as 0.23 for low wind speed condition.
# Drag Reduction in Compressible Channel Turbulence with Periodic Interval Blowing and Suction
## 1 Introduction
Opposition control facilitates flow separation behind the bump, whereas uniform blowing mitigates it. According to the experiment by [PERSON] and [PERSON] [15; 16], the skin friction will not immediately revert to its pre-control level after the cessation of control, but will gradually develop over a specified distance. The phenomenon is called the \"memory effect\" and it is inspiring for interval control. In comparison to global control, interval control arranges the controlled regions by intervals, hence diminishing energy expenditure for blowing and suction while preserving drag reduction and lowering the cost and complexity of installing the control device in engineering applications. [PERSON] et al. [17] studied the skin friction distribution in incompressible spatially developed turbulence. The skin friction diminished as the blowing was applied, and it recovered when the blowing stopped. The skin friction downstream of the controlled region was still lower than the uncontrolled case. [PERSON] et al. [18] and [PERSON] et al. [19] explored the drag reduction of micro-hole blowing on a flat-plate turbulent boundary layer at \(Ma=0.7\) and \(Ma=2.25\), respectively, and reached similar conclusions. [PERSON] et al. [20] employed four spanwise blowing strips at intervals in compressible channel turbulent flows at \(Re_{\text{b}}=3000\). The skin friction declined in the first few strips and rose in the last strip at \(Ma=0.8\). However, the skin friction dropped in the non-blowing zones and recovered in the blowing zones at \(Ma=1.5\). [PERSON] et al. [21] and [PERSON] et al. [22] verified that the \"memory effect\" exists after blowing on the Clark-Y airfoil model. Furthermore, the \"memory effect\" has also been discovered in riblet [23,24] and porous media [25,26] control. The design of interval control has great potential for drag reduction. Prior research has demonstrated that augmenting the blowing intensity by global blowing can enhance the drag reduction rate [9]. Nonetheless, in the context of interval blowing, the blowing area also influences the drag reduction capability. There is a deficiency in the literature about the critical factor that impacts the drag reduction rate for interval blowing. Our previous study [20] showed that interval blowing can diminish friction drag. However, we have not performed a comprehensive examination of how blowing area and blowing intensity influence the drag reduction effect. In this paper, a physical model of fully developed turbulence in a channel with infinite streamwise length is studied, the key factor that affects the drag reduction rate is explored, and the drag reduction effect of interval blowing and suction is analyzed. The paper is organized as follows: Section 1 introduces the research progress in blowing and suction control. Section 2 presents the physical model and the details of the computational codes. Section 3 clarifies the drag reduction effect of the interval control and analyzes the mechanism. Finally, Section 4 summarizes the main conclusions of this paper.